Astral Codex Ten

North Carolina

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North Carolina

Article

North Carolina is a recurring place in the Astral Codex Ten archive, appearing 15 times across 15 issues between February 24, 2021 and April 01, 2026. The archive places it in contexts such as “deep in the countryside of North Carolina”; “governor of North Carolina”; “cities in … North Carolina also have okay climates without too many homeless”. It most often appears alongside New York, California, Scott.

Metadata

  • Category: Places
  • Mention count: 15
  • Issue count: 15
  • First seen: February 24, 2021
  • Last seen: April 01, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Book Review: Fussell On Class
February 24, 2021 · Original source
Where then may a member of the top classes live in this country? New York first of all, of course. Chicago. San Francisco. Philadelphia. Baltimore. Boston. Perhaps Cleveland. And deep in the countryside of Connecticut, New York State, Virginia, North Carolina, Pennsylvania and Massachusetts. That's about it. It’s not considered good form to live in New Jersey, except in Bernardsville and perhaps Princeton, but any place in New Jersey beats Sunnyvale, Cypress, and Compton, California; Canton, Ohio; Reno, Nevada; Cheyenne, Wyoming; Albuquerque, New Mexico; Columbus, Georgia, and similar army towns.
Biography of Jason Shea, 44th US President
April 13, 2022 · Original source
For example: during his career, Xi served as party secretary of four different areas: Zhengding, Fujian, Zhejiang, and Shanghai. A faithful conceptual translation would have had Shea serving as county supervisor of some small county in Virginia, then mayor of Miami, then governor of North Carolina, then governor of New York. This career progression doesn’t make a lot of sense in a US context (except for this guy!), so I turned him into a consultant in Virginia, Florida, and North Carolina before becoming NYC mayor. Maybe I should have just let him be governor of a bunch of states and let it be implausible, I don’t know.
Inline links: this guy
Highlights From The Comments On San Fransicko
June 29, 2022 · Original source
California and Seattle are among the rare US cities where it never gets unbearably hot or cold, and do have a lot of homeless people. But cities in Florida, Virginia, and North Carolina also have okay climates without too many homeless, so I’m not sure how much to update on this.
Spring Meetups Everywhere 2023
April 10, 2023 · Original source
RESEARCH TRIANGLE (RALEIGH-DURHAM), NORTH CAROLINA, USA Contact: Logan Contact Info: RTLW[at]googlegroups[dot]com Time: Thursday, April 13th, 07:00 PM Location: By the Ponysaurus Brewing Company Coordinates: https://plus.codes/8773X4Q4+Q2C
Inline links: https://plus.codes/8773X4Q4+Q2C
The Question Of Separatism
May 19, 2023 · Original source
Finally, an oversized capital force creates an artificial city region. In the US, the Tennessee Valley Authority was a Depression Era program to develop a poor region using federal government money. The hydroelectric dams and other infrastructure that the money bought seemed to be great successes at first, and to be sure they did reduce poverty. But problems later appeared, and today the region isn’t particularly dynamic, in addition to being riddled with environmental issues. Jacobs explains that the federal aid could never truly help, because the Tennessee Valley has always lacked an import-replacing city. Subsidies, grants, and loans give at best the illusion of development. None of these five types of rural regions tend to do great in the long run, unless they manage to generate an import-replacing city. But at least they receive something from distant cities. It’s far worse when a region is untouched by city forces at all, as Bardou was for a long time. Or as was a hamlet in North Carolina that Jacobs calls “Henry” for anonymity reasons, but which we can safely reveal to be Higgins, in the Appalachian region. Here is what Higgins looked like in 2013 on Google Street View: There is a nice modern road in that screenshot, but between its 18th-century founding and the 1920s, there wasn’t even a path that a horse-drawn wagon could use, and so Higgins was extremely isolated. It barely sold anything to anyone outside, and accordingly imported very little. The people lived from subsistence farming. Their lives were so difficult, so focused on sheer survival, that they gradually forgot many of the skills and techniques that their British ancestors had, like candle making, weaving from a loom, and even masonry. When Jacobs’ aunt arrived as a Presbyterian missionary in 1922, and suggested that they build a church out of stone, the people of Higgins confidently stated that this was impossible: mortar just wasn’t strong enough. “These people came of a parent culture that had not only reared stone parish churches from time immemorial, but great cathedrals,” Jacobs writes, and yet eventually they forgot that stone buildings were a possibility at all. Such is the fate of regions that get cut off from cities. Jacobs calls them bypassed places. Sometimes these places are entire countries, such as Ethiopia, once the seat of an empire, but which as of the 1980s had barely any links to cities except its own backward ones. Unsurprisingly, Ethiopia has high prices (for Ethiopians) and too few jobs. That will always be so, unless one of its cities can start the process of import replacement. III. Should Everything Be a City-State? That was roughly the first half of the book. After that, Jane Jacobs discusses various consequences of her theory, including why decline happens and how we can, in theory, prevent it. We’ll get there — but first, it’s time for a detour through the other book, The Question of Separatism, which provides a great case study of Jacobs’s ideas. After an introductory chapter in which Jacobs acknowledges that separatism always makes everyone emotional, and warns that she’s going to study it in a dispassionate manner anyway, she starts by describing the issues in Quebec and Canada through a specific lens. You can probably guess which lens. That’s right — cities. To her, the question of Quebec separatism is primarily the question of how the two main cities in Canada, Toronto and Montreal, have coexisted and will coexist in the future. At this point you need at least a basic understanding of Canadian history. Here’s a quick primer, focusing on those two cities. Canadian History Speedrun (Jane’s Version) Canada, a word that used to refer to the large valley around the St. Lawrence river and the Great Lakes, was originally a colony of the Kingdom of France. Then the Kingdom of Great Britain conquered it in 1760. For various reasons, most of the French settlers stayed in Canada rather than emigrating to France or being deported, so at first, a small British elite ruled over a mostly French-speaking and Catholic colony. However, immigration from the British Isles, as well as from the newly seceded United States (loyalists who wanted to live in a monarchy rather than a republic for some reason) eventually tipped the linguistic and cultural balance. The population sorted itself such that the lower part of the valley (what is now Quebec) remained French, while the upper part (what is now Ontario) became English. The exception to this trend was the city of Montreal. Although located in Quebec, it became an English-speaking city and the hub for the British merchant elite. For at least a hundred years, it was the main city in Canada across almost all metrics: population, wealth, manufacturing, political influence. In the middle of the 20th century, Montreal grew enormously and became French-speaking again, owing to immigration from rural Quebec. It became the center of Quebecois culture and, with its increasingly educated population, the breeding ground for new ideas, including separatism. At the same time, the main city in Ontario, Toronto, was growing even faster. Immigrants from all over Canada and other countries poured into it (including Jane Jacobs herself). Sometime around 1970, it became bigger and wealthier than Montreal, and replaced it as the main economic hub. Many people attribute this to the rise of Quebec separatists, which supposedly scared the Anglo elite of Montreal into moving all the banks and companies to Toronto, and, to be sure, some of that happened — but of course, Jacobs prefers explanations that rely on city economics. One of the reasons for Toronto's economic and demographic growth is that it became the nexus of what Jacobs calls a conurbation, and would have called a city region if we were in the other book. In case you craved another concrete example of a city region, here’s a map of Ontario with two ways to define Toronto’s so-called “Golden Horseshoe” (Toronto itself is just the tiny strip in the middle of the red area, next to the lake): Meanwhile, Montreal never generated a conurbation or significant city region. This is Jacobs’s main hypothesis for why it was overtaken by Toronto, though she doesn’t give a lot of detail on why it happened. In any case, the result was that Montreal lost its status as the economic capital of the country. It became a regional city. The problem is that regional cities tend to do poorly. The nature of nations is to centralize everything in one place (we’ll come back to this). That’s why Paris has a large and rich city region, but Lyon and Marseille don’t. That’s why London looms so large in the UK’s economy while Glasgow or Manchester now contribute very little. There’s nothing wrong per se with being an economically stagnant regional city. Such cities can be fine places. When they’re the center of a supply region, like Calgary and Edmonton in oil-rich Alberta, they can even be wealthy. The complication for Montreal, though, is that its previous status as the main Canadian metropolis made it grow too large for this purpose. Yet, at the same time, Montreal plays an outsized cultural role for French-speaking Canadians — one that Toronto doesn’t even come close to fulfilling. So, Jacobs sees only decline for Montreal. And she thinks this means decline for Quebecois culture generally. Without a strong import-replacing city, Quebec will become a patchwork of supply regions, regions that workers abandon, or transplant economies, like the poverty-stricken Atlantic provinces in eastern Canada already are. Either the Quebecois resign themselves to this fate, she says, or they fight it — and the only true way to fight it is to declare independence. As of the 1980 referendum, she thinks they should go for independence. Generalized Separatism Quebecers did not go for independence, neither then in 1980 nor in 1995 when they voted on the question again. If they had, it would probably have been an example of a peaceful secession. Jacobs points out that there haven’t been many of those, if you exclude the decolonization of overseas imperial possessions (like Canada from Britain). Non-peaceful secessions have been common, but in those cases the destructiveness of war tends to overshadow everything else, economically speaking. In fact that might be the main reason most of us intuitively dislike separatism: we associate it with conflict. But peaceful non-colonial secessions do happen. Since 1980 there have been several more cases, like Czechia and Slovakia. When Jacobs wrote her book, though, the only good example she could think of was the independence of Norway from Sweden in 1905. She tells a great account of the process, noting that the outcome wasn’t predetermined: Sweden didn’t want to lose its western province, and did what it could to contain Norwegian nationalist sentiment. But Norwegian nationalist sentiment won — and importantly, both Norway and Sweden seemingly benefitted. Neither of them was particularly rich in the 19th century, and Norway was in fact dirt poor, which is why so many Norwegians escaped by emigrating to North America. Yet after the dissolution of their union, the two countries developed quickly, and both are now among the wealthiest countries in the world. They certainly didn’t disintegrate. (Of course, in Norway the wealth is due in large part to the oil that they discovered in the late 1960s. But they were pretty advanced by that point already — advanced enough that they could use the oil to develop their own industry, rather than get rich quick by exporting it raw, which is what keeps many countries trapped as supply regions.) When people argue against separatism, they often tout the benefits of being large. A Canada that would be split in two would mean smaller markets, and a weaker political counterweight to the United States. (Not to be mean to Canadian readers, but this argument seems delusional to me — I don’t think Americans currently see Canada as a political counterweight of any significance.) It would certainly be less prestigious. Large size, Jacobs says, is associated with power, and we admire power. We love slogans like “unity makes strength.” But after the medium-sized country of Sweden-Norway became the two smaller countries of Sweden and Norway, they both did well. Small size is less powerful, but it has its own advantages, such as nimbleness and ability to fail non-catastrophically. Small size also allows more diversity in cultural and economic matters, and here Jacobs waxes philosophical, pointing out that favoring diversity over uniformity is a recent, post-Enlightenment idea that has not yet been fully embraced in politics. We can see analogs everywhere. Europe, split into numerous small countries from the Middle Ages onward, became far more advanced than China, which has been unified more often than not. The city-states of ancient Greece and Renaissance Italy are seen as golden ages of Western civilization, even if they weren’t part of larger political units and therefore constantly went to war with one another. In business, large companies are impressive and powerful, but people always complain that Google or Microsoft have become stagnant and that the best place to work is tiny startups of about 2 cofounders and 4 employees. In biology, humans are more successful than numerous larger animals, and in terms of raw numbers, small animals like rats or insects are the most successful of all. Jacobs’s point isn’t that smaller is always better. Her point is that the converse statement, “bigger is always better,” is false — despite how intuitive it feels for political entities. Just like we don’t view a small nation like Switzerland or Singapore as a failure of unity, we (and in particular, Canadians) shouldn’t see the secession of a place like Quebec, if it’s done peacefully and democratically, as a failure either. Still, some people in online reviews of the book complain that this argument is a bit thin, especially considering that it serves as the foundation for the later chapters (which are more directly about late 1970s Quebec politics). Sure, small is beautiful, but large states are great for stability, peace, markets, whatever. If the potential benefits of small national size are Jacobs’s strongest argument, then we can breathe a sigh of relief and go back to agreeing that separatism is bad. Pointing out the widespread bias in favor of unified political entities does seem valuable to me, but okay, fair enough. Does Jacobs have deeper reasons why separatism might be a good idea in general? Yes, and for this we go back to the second half of Cities and the Wealth of Nations. Why Nations and Empires Fail Our breathing rate is regulated through a feedback mechanism. Too much carbon dioxide in the blood, or too little oxygen, and the brain stem commands the diaphragm to accelerate breathing. Once the levels are back to normal, the brain stem receives this feedback and slows breathing down again. Now, Jacobs asks, imagine an impossible creature: ten people, all doing their own thing, but whose breathing is somehow regulated by a single brain stem. The feedback the brain stem receives is a consolidated average of everyone’s carbon dioxide and oxygen levels, and the breathing rate the stem decides on is applied to all ten people, regardless of whether they’re sleeping or playing tennis. This, to put it mildly, wouldn’t work. This creature is an analogy, representing a nation. The ten people are its individual cities, and the breathing rate is the cities’ economies. If it sounds like a stupid analogy, that’s because it is: “I have had to propose a preposterous situation,” writes Jacobs, “because systems as structurally flawed as this don’t exist in nature; they wouldn’t last.” Nor do they exist in machines we design; they wouldn’t work. But “nations, from this point of view, don’t work either, yet do exist.” The feedback mechanism that fails to work properly in a nation is currency. A currency always fluctuates according to the exports and imports of the area where it circulates. Let me use the Republic of Venice and its ducat as a toy example, because the coins look nice: Whenever Venice produces something (like salt) and sells it abroad, foreigners need ducats to buy the exports, so the demand for ducats increases. When Venice buys something from abroad, it needs to use foreign currencies, so the demand for ducats decreases. Add up everything that Venice exports and imports, and you get either a trade surplus (more exports than imports) or a trade deficit (more imports than exports), which determines the value of the ducat relative to other currencies. In both cases, a negative feedback loop restores balance over time, just like our brain stem does with carbon dioxide levels. A trade surplus, and therefore a strong ducat, means that when foreigners want Venetian salt, it’s expensive. So Venice’s exports decrease, while imports increase, since Venetians can use their valuable ducats to buy stuff cheaply from abroad. Conversely, a trade deficit makes exports a bargain for foreigners and imports expensive for Venetians. This feedback loop is great. It’s exactly what a city needs to trigger the crucial import replacement process. When exports decrease and a trade deficit begins (maybe because Constantinople found a cheaper source of salt somewhere else), the weak ducat means that Venice is less able to afford the resources and manufactured goods it used to import. The people of Venice don’t want to have less of those goods, though, so they figure out ways to produce some themselves — that is, they do import replacement. Later they will be able to export the output of the newly expanding industries too, strengthening the ducat and continuing the cycle. Currencies, Jacobs explains, function as automatic tariffs (to protect local industry from foreign imports) and automatic export subsidies (to encourage local industry to export). They are “automatic” because of the feedback mechanism. Just like an accelerated breathing rate, they take effect exactly when they are needed — and no longer. … Or so they should, except that import replacement, as we discussed, is a city process. Whereas most currencies are national or supranational. National currencies work well for city-states, like the Republic of Venice or today’s Singapore. But in large nations, which, remember, are not the fundamental unit of economic life, they mess everything up. Take a city like Detroit. When Detroit’s exports (primarily cars) decrease, Detroit gets no feedback about this, because its currency is the United States dollar, and the United States dollar’s value depends on much more than Detroit. It depends on other cities whose foreign exports might be increasing at the moment. And on rural regions that are selling resources like oil abroad. Also, trade between Detroit and other cities that use the United States dollar — i.e., American cities — is structurally unable to provide any feedback whatsoever. So Detroit doesn’t get the signal that it should buy less stuff from other cities and replace the missing imports with local production. Instead, it just declines. Jacobs hypothesizes that this issue of national currencies is at the root of every large country’s economic troubles. It is why nations and empires always centralize everything into one large city, whether that’s Paris, London, Tokyo, or Toronto, or ancient Rome: that city, being the largest, is simply the only one for which national-level currency feedback works fine. The rest of the nation or empire, then, declines. But of course, nations and empires don’t accept this. They care about the economic well-being of their peripheral regions, sometimes out of genuine concern for the people there, sometimes out of fear that they rebel or hold independence referendums. So nations and empires will embark on every possible solution to reverse the decline. All of their solutions will look like good ideas at first, and yet fail at helping the peripheral regions. Worse, these solutions will weaken the cities, thereby destroying the only real wealth of the country and bringing untold hardship for everyone. Eventually the nation or empire will disintegrate, as nations and empires always do, and always will. Jacobs calls these false solutions transactions of decline. She identifies three types, and, content warning, you might not like some of them depending on your political sensibilities. Sustained military production is a transaction of decline. Permanent military bases and garrison towns are a special kind of settlement: they import a lot and export nothing. Superficially, producing weapons and supplies for the military seems like a good deal for some cities — Jacobs gives the example of Seattle, which, before Microsoft and Amazon were a thing, depended mostly on making military aircraft. But because nobody in a military base ever tries to replace those weapons and supplies with their own production, the trade is sterile in terms of economic development. In a sense, the wealth is slowly “drained” from cities. Large empires are especially prone to this: eventually all of their wealth is destined to the military just to keep the empire together.
Inline links: Higgins, https://substackcdn.com/image/fetch/$s_!d77P!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd8a42329-e67b-47b5-9e68-757112957dbb_1600x718.png, https://substackcdn.com/image/fetch/$s_!Qj1l!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff727cfae-ec34-42c4-b509-9a25f4126f2d_1600x834.png, https://substackcdn.com/image/fetch/$s_!T392!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff9795745-8a40-40c1-8173-5296c13eef3b_1600x1553.png, https://substackcdn.com/image/fetch/$s_!zETj!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3e99b106-d34e-4a49-93a8-553a8f57952a_1600x1130.png, https://substackcdn.com/image/fetch/$s_!YeXj!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6fe681e6-b571-4f4c-aa90-313f77fc1dbb_750x359.png
Higgins, North Carolina screenshot: from Google Street View.
Inline links: Google Street View
Meetups Everywhere 2023: Times & Places
August 25, 2023 · Original source
ROCHESTER, NEW YORK, USA Contact: Jens Contact Info: jensfiederer[at]gmail[dot]com Time: Saturday, October 14th, 3:00 PM Location: Spot Coffee Coordinates: https://plus.codes/87M45C42+H9 North Carolina ASHEVILLE, NORTH CAROLINA Contact: Vicki Williams Contact Info: VickiRWilliams[at]gmail[dot]com Time: Saturday, September 16th, 11:00 AM Location: Lake Julian Park. We'll try to grab a picnic table near the playground but rsvp for precise update if you don’t want to hunt for the sign. Coordinates: https://plus.codes/867VFFJ6+2G5 Notes: Please rsvp so I can update on our exact location and in case we need to reschedule for weather.
Inline links: https://plus.codes/87M45C42+H9, https://plus.codes/867VFFJ6+2G5
ASHEVILLE, NORTH CAROLINA Contact: Vicki Williams Contact Info: VickiRWilliams[at]gmail[dot]com Time: Saturday, September 16th, 11:00 AM Location: Lake Julian Park. We'll try to grab a picnic table near the playground but rsvp for precise update if you don’t want to hunt for the sign. Coordinates: https://plus.codes/867VFFJ6+2G5 Notes: Please rsvp so I can update on our exact location and in case we need to reschedule for weather.
Inline links: https://plus.codes/867VFFJ6+2G5
CHARLOTTE, NORTH CAROLINA, USA Contact: Cat Contact Info: cat[dot]esposito[at]gmail[dot]com Time: Tuesday, October 10th, 6:30 PM Location: Free Range Brewing - 2320 N. Davidson St., Charlotte, NC. I'll be in the outdoor seating section that is in front of the residential apartment buildings and will have an ACX MEETUP sign with me. Coordinates: https://plus.codes/867X65RP+6P Notes: It's a brewery that typically serves food on Tuesday nights.
Inline links: https://plus.codes/867X65RP+6P
The Psychopolitics Of Trauma
January 25, 2024 · Original source
Experiencing repeated or extreme exposure to aversive details of the traumatic event(s) (e.g., first responders collecting human remains; police officers repeatedly exposed to details of child abuse). This is already quite broad! The victim doesn’t need to have anything bad happen to them - just be threatened with it. And they don’t need to personally be the victim of the threat. They can learn that it happened to someone close to them, or they can just hear about it happening to someone else. A police officer who hears about child abuse may be a trauma victim! The DSM’s job is to draw a medico-legal boundary - this counts, but that doesn’t. The real world has no obligation to obey the DSM, and often doesn’t. For example, can someone be traumatized by something happening to a distant family member? It would be insane to think this has never happened, and that some law of nature limits it to close family members. The DSM is just using the heuristic that probably it’s worse when it’s someone close to you. It goes on: [Part 4] does not apply to exposure through electronic media, television, movies, or pictures, unless this exposure is work-related. Did someone prove it was a natural law that you can only be traumatized by seeing a story on TV if it’s for work? Or is this another unprincipled compromise? People not involved in the DSM, unbound by medicolegal considerations, have added all kinds of stuff to this basic definition. For example, even though it’s not in the strict DSM definition, psychologists almost universally agree that emotional abuse can be traumatizing. And in the current social climate, inevitably people have started talking about collective trauma, eg institutional racism may be traumatizing for some individual black person even if they personally have never been victimized in any dramatic way. The knowledge that people hate their whole group serves as an adequate proxy for anybody abusing them personally. Can you chain all of these exceptions together? Can witnessing a family member suffering emotional abuse be traumatizing? Can learning secondhand about someone encountering institutional racism be traumatizing? Can you be traumatized by hearing on TV that someone was emotionally abused on account of their race? Only if it’s part of your job? At this point the nice crisp distinctions of the DSM are starting to feel a little artificial. I think of all of this in a deflationist, spectrum-y type of way. Anything can be traumatizing if it gives you strong negative emotions and makes you feel helpless and victimized. The DSM points to some categories that are especially likely to cause this kind of reaction. Other people have added their own. But if something you hear on TV makes you feel victimized and helpless, then sure, go ahead and call it traumatizing. If Trump’s election made you feel victimized and helpless, then I’m prepared to say “trauma” is a potentially fruitful lens through which to investigate this response. (I’m not saying that Trump’s election was inherently traumatizing, or that trauma was the correct response. If you prefer, you can think of it as a condemnation of the media for irresponsibly fanning fear of Trump. I’m just saying, without trying to lay blame, that lots of people did experience feelings of fear and helplessness around Trump’s election.) III. I didn’t personally feel traumatized by Trump’s election. My own story, which I don’t claim is atypical or sympathetic in any way, is that in college a bunch of people tried to cancel me for something I’d intended to be an anti-racist joke, but which apparently didn’t come out that way. Former friends turned against me, I got a few death threats, and I was told to attend a criticism session at a local social justice meeting group (which I foolishly did; I thought people would realize I was cooperative and agreed with them, and so lay off - obviously this didn’t work). I briefly considered dropping out of college to avoid the hatred; instead I spent a month locked in my room, waiting for the storm to blow over. It was the worst experience of my life. Ever since then, when I read arguments promoting social justice and cancel culture, or saying that their victims are probably bad people and shouldn’t be allowed to defend themselves, I get all kinds of easily noticeable unpleasant bodily and emotional reactions. When I read good arguments against these positions, I get some kind of nice calm feeling, like that I’m suddenly safer and the world has brightened a little bit. I try as hard as I can to approach these kinds of issues fairly, but it wouldn’t surprise me if I make more of the “five Democrats and eight assault weapons” style reasoning errors there than I would on some boring topic like taxes. Of course, I hear similar stories from people on the other side of this particular culture war. A typical example (this is a pastiche of many people) would be a transgender person who sometimes gets harassed when they try to go into public restrooms. Even if it never gets beyond catcalling, they remember all the stories they read about trans people getting murdered, and even looks of disapproval feel like they carry the potential for physical violence. Then they hear about trans bathroom bills in North Carolina or wherever and absolutely see red; they feel like Society as an abstracted entity is trying to deny their right to exist. Then they invent entirely new kinds of social technology to prevent themselves from ever having to talk to or interact with the sort of people who would support such a thing. Most people haven’t personally been cancelled or discriminated against, and they might not have stories like these. But they might feel like society is “threatening” them with these kinds of experiences. Or they might have “close family” or “close friends” who qualify. Or they might have heard about them on TV. (In a work-related context? Sure, let’s say yes.) But also, there’s the collective trauma exemption! Everybody belongs to various groups - black people, white people, Jews, Christians, men, women, LGBTs, gun owners, socialists, cops. Parts of each of these groups have developed narratives about how they’re being singled out for special persecution by the people in power. You probably believe that some of these groups’ narratives are valid, and others are false and offensive. That doesn’t matter. The important thing is that (some of) the group members believe it. The DSM is quite clear that people react to threatened trauma, not actual trauma. If some very silly person works himself up into a frenzy believing he’s being abused and persecuted because he eats eggs for breakfast, that’s potentially traumatizing, even if his concerns have no basis. But also, everyday political debate crosses lines that would qualify as emotional abuse in any other sphere of life. People get told they’re disgusting or idiotic or deserve to die. They have to watch as powerful rivals plot openly how to ostracize them from polite society. Groups of their enemies get together to spread the rumor that they are Satanists, Nazis, or pedophiles. They have their views twisted into totally false claims that they want to murder children, which then “go viral” to people who otherwise know nothing about them. If you’re not famous, this might not happen to you personally - nobody says “John Smith is a Nazi pedophile”. But John Smith might be a socialist, and someone might say “All socialists are Nazi pedophiles”. If we believe that racism can traumatize minority individuals even if they’re not personally named in the stereotypes, we should believe that the discourse around socialism can traumatize socialists, even if they’re not personally involved. I’m probably not describing this well, so I can only beg you to supplement my inadequate words with your lived experience. All bullying sounds trivial when you’re not involved. “He called me a fatty on the playground!” Well, whatever, laugh it off. But somehow from the inside, iterated over many experiences, coming from people you perceive as more socially powerful than you, it creeps up on you, starts getting power you definitely don’t remember giving it. Think of some discourse you’re involved in, some issue you feel really invested in, and think about the people you find most unfair and enraging on the other side. I dunno, either you’ve had this experience or you haven’t. I think a lot of people feel persecuted and threatened by politics, a lot of people feel emotionally abused by politics, and a lot of people feel like they’ve had vicarious experiences of people they identify with being harmed by politics. This isn’t enough for a formal PTSD diagnosis - they probably didn’t watch the relevant TV news segments in a work-related context. But it might be enough to start doing some really unhealthy things to their brains. IV. Here’s what the DSM has to say about some symptoms of PTSD: B4: Intense or prolonged psychological distress at exposure to internal or external cues that symbolize or resemble an aspect of the traumatic event. The popular term for criterion B4 is “a trigger”. For example, if you were raped, you might be triggered by hearing someone describe rape. This is justification for so-called “trigger warnings” in books and movies. Triggers have long since jumped from the lexicon of PTSD to the lexicon of politics. Left-wingers describe exposure to right-wing ideas or symbols as “triggering”. Right-wingers try to avoid the terminology, because it sounds too leftie, but they have the experience so often that lefties asking right-wingers “oh, are you TRIGGERED?” has become a meme. Twitter searches for “triggered” are an interesting anthropological experience. A Google search brought up this lovely t-shirt. I think eBay’s policy of promoting inclusiveness by displaying shirts on ethnically diverse models may have failed them in this case. This is only the tip of the iceberg. Donald Trump Jr has a book called Triggered, and a biweekly TV show of the same name. Sheila Jeffreys’ biography is called Trigger Warning: My Radical Feminist Life. Jeffreys and Trump Jr may not have much else in common, but they are united by a shared appreciation for applying this technical psychiatric term to politics. I think this makes the most sense if political triggering and psychiatric triggering are literally the same thing because political toxicity is a subspecies of PTSD. D2: Persistent and exaggerated negative beliefs or expectations about oneself, others, or the world. Do I even need to explain this one? D3: Persistent distorted cognitions about the cause or consequences of the traumatic events that lead the individual to blame himself or others. As stated, this doesn’t really apply to politics. But I claim this is an overly restrictive description of the true problem, which is a general distortion of cognition around traumatic stimuli. See for example Reasoning, trauma, and PTSD: insights into emotion–cognition interaction. Here the researchers make people solve math/logic puzzles with five apples and eight oranges or whatever; as usual, most people do fine. Then they change the content to traumatic stimuli, like five rapists and eight abusers. Nobody is particularly happy about this change, but traumatized people seem to do worse when the stimuli relate to their own trauma. This is an exact analog to the “five Democrats and eight assault weapons” task discussed above; I don’t know if one line of research inspired the others, but they show some similar results. Other people have even more general findings. You may remember the Stroop Effect, where people have to say the color of words without getting distracted by their content. One variant is the Emotional Stroop Effect, where instead of giving color words (“yellow”, “red”, etc), you use emotional words and traumatic stimuli. Traumatized people tend to do worse at Emotional Stroop tasks relating to their specific trauma. See Modification of cognitive biases related to posttraumatic stress: A systematic review and research agenda. See also The Precision Of Sensory Evidence for a discussion of how this effect might happen. E1: Irritable behavior and angry outbursts (with little or no provocation) typically expressed as verbal or physical aggression toward people and objects. As seen at your family Thanksgiving table. Politics makes otherwise kind people into angry jerks. E3: Hypervigilance This is defined as a heightened awareness of surroundings, constantly scanning for danger, and misinterpreting innocuous stimuli as threatening. Wikipedia describes it as “there is a perpetual scanning of the environment to search for sights, sounds, people, behaviors, smells, or anything else that is reminiscent of activity, threat or trauma”. Dog whistles. Microaggressions. The hallmark of the advanced political partisan is the ability to describe everything the other side (or neutral third parties) do as secretly a political offense, and to reduce every possible situation to their issue of choice. For the past ten years, I’ve been involved in the anti-AI-existential risk movement, and have gotten to know other people in this movement pretty well. I can say with high certainty that the number one motive of these people is that they do not want to be killed by robots. Still, over the years people have ascribed every possible motive to us except that one, for example: It’s a plot by Big Tech to distract from other harms they are committing.
Inline links: emotional abuse, institutional racism may be traumatizing, entirely new kinds of social technology, https://substackcdn.com/image/fetch/$s_!G65x!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9be25ee8-5d77-4a57-a07d-efff5ff66b44_589x461.png, https://substackcdn.com/image/fetch/$s_!i3Mp!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F69cd8c8a-3f2b-4842-b8fd-1455aa1310bf_591x587.png, Triggered, Trigger Warning: My Radical Feminist Life, Reasoning, trauma, and PTSD: insights into emotion–cognition interaction, Modification of cognitive biases related to posttraumatic stress: A systematic review and research agenda., The Precision Of Sensory Evidence, Dog whistles
Practically-A-Book Review: Rootclaim $100,000 Lab Leak Debate
March 28, 2024 · Original source
Lineage A (left) was used by the Minoan Cretans, but has never been deciphered. Lineage B (right) was used by the Mycaeneans for lists of palace goods. This matches Saar’s story above. The lab leaked to somewhere else in Wuhan, not the wet market. The virus spread undetected in the population for a while. During this time, it mutated to Lineage B. Then one of the people with Lineage B went to the wet market and started a superspreader event. The authorities sampled the patients, found Lineage B, then started looking elsewhere. Later they detected some of the earlier Lineage A cases. The market is unlikely to be the origin of the pandemic, because the original Lineage A strain wasn’t found there. Peter: Although Lineage A is evolutionarily older, Lineage B started spreading in humans first. We know this because Lineage B is more common. Throughout the early pandemic, until the D614G variant drove all other strains extinct, a consistent 2/3 of the cases were B, compared to 1/3 A. Both strains spread at the same rate, so the best explanation is that B started earlier than A. Since COVID doubles every 3-4 days, probably Lineage B started 3-4 days earlier than Lineage A, which explains why it’s always been twice as many cases. But also, Lineage B also has more internal genetic diversity than Lineage A. In general, older viruses have more genetic diversity (the “molecular clock”). This is further evidence that B started spreading first. Pekar 2022 and Pipes 2021 do analyses with known parameters for spread rate and diversity, and find 90%+ odds that Lineage B was the first one in humans. Why did the older strain start spreading later? Probably the virus crossed from bats into raccoon-dogs on some raccoon-dog farm out in the country. It spread in the raccoon-dogs for a while, racking up mutations, including the (less mutated) Lineage A strain and the (slightly more mutated) Lineage B strain. Then several raccoon-dogs were taken to Wuhan for sale, including one with Lineage A and another with Lineage B. The one with Lineage B passed its virus to humans earlier. Then 3-4 days later, the Lineage A one passed its virus to humans. Lineage A was first found in a Wuhan neighborhood right next to the wet market (closer to the wet market than 97% of Wuhan’s population). Again, it would be a bizarre coincidence if a lab leak pandemic was first detected at a wet market. But it would be an even more bizarre coincidence if a lab leak pandemic separated into two strains, and both were first detected at a wet market! Although no known wet market cases were Lineage A, a positive Lineage A environmental sample was found at the wet market, and everyone agrees most cases went undetected. So maybe the Lineage B raccoon-dog spread its virus to a vendor, and that sub-strain mostly stayed in the market. But the Lineage A raccoon-dog spread its virus to a customer, who went back to his house nearby, and that strain spread in the neighborhoods next to the market. This is the only story that explains the evolutionary precedence of A, the greater spread and older molecular clock of B, and the fact that both strains were first found very close to the wet market. Yuri/Saar: Lineage B could be more common and diverse because it got the advantage of a super-spreader event in the wet market. There are a few scattered cases of intermediates between A and B, and a few other scattered cases of lineages that seem even more ancestral (ie closer to the bat virus) than either. This doesn’t make sense in a double spillover hypothesis. But it does make sense if the lineages separated in human transmission somewhere between the lab and the first super-spreader event at the wet market. Peter: Again, the wet market wasn’t a super-spreader event. COVID spread in the wet market at exactly its normal spread rate, doubling about once every 3.5 days. Stop calling the wet market a super-spreader event. The scattered cases of “intermediates” are sequencing errors. They were all found by the same computer software, which “autofills” unsequenced bases in a genome to the most plausible guess. Because Lineage B was already in the software, depending on which part of a Lineage A virus you sequenced, you might get one half or the other autofilled as Lineage B, which looked like an “intermediate”. We know this because all the supposed “intermediates” were partial cases sequenced by this particular software. We can confirm this by noting that there are too many intermediates! That is, where Lineage A is (T/C) and Lineage B is (C/T), the software found both (T/T) “intermediates” and (C/C) “intermediates”. But obviously there can only be one real intermediate form, and we have to dismiss one or the other. But in fact we can dismiss both, because they were both caused by the same software bug. The scattered “progenitor” cases - those closer to the ancestral bat virus than either A or B - are reversions, ie cases where a new mutation in the virus happened to hit an already-mutated base and shift it back towards the ancestral virus. We know this because all of these “progenitors” were scattered cases found months after the pandemic started, often in entirely different countries from Wuhan. If these were real progenitor viruses, they would have either fizzled out or exploded into a substantial portion of all cases, not be found one time in one guy in Malaysia. Given the number of mutations the virus developed over the course of the pandemic, it’s inevitable that some of them would be mutations that bring it closer to the original bat virus, and in fact we find the number of “progenitors” found very nicely matches the number of progenitor-appearing viruses we would expect by chance. And in many cases, we know the “progenitors” are newer than the original lineages, because they also have some of the later mutations that Lineage A or B picked up along the way, alongside their apparent ancestral-bat-virus-like mutations. Session 2: Viral Genetics Yuri: Two years before COVID, scientists at the Wuhan Institute of Virology, together with colleagues at the University of North Carolina, sent in a grant proposal for the DEFUSE program. This program, intended to locate and better understand potential future pandemic viruses, involved going into bat caves and collecting new coronaviruses. Once they had them, they would do gain-of-function: specifically, they would add a furin cleavage site to make them more infectious and see what happened. (quick interlude: COVID’s spike protein has two sections: one binds to human cells through the ACE2 receptor, the other helps fuse with the cell after binding. In order to avoid the immune system, it hides both of these into one spike. But when it reaches a cell, it needs to separate them again. It takes advantage of a human respiratory enzyme, furin, to do the separation - this also ensures that it only infects its primary target, human respiratory cells. The part of COVID that lets it get separated by furin is called the “furin cleavage site”. COVID’s bat-virus ancestors were gastrointestinal viruses; the addition of a furin cleavage site was what made them respiratory viruses.) We’ve found two close relatives of COVID: bat viruses called RATG-13 and BANAL-52. In particular, COVID looks more or less like BANAL-52 plus a furin cleavage site. There are 1500 sarbecoviruses, members of the family of viruses that includes SARS and SARS2/COVID. None of them except COVID have furin cleavage sites. BANAL-52, COVID’s closest ancestor, doesn’t even have anything resembling one that could mutate into a functional furin cleavage site like COVID’s. Instead, COVID - which mostly just resembles BANAL-52 with a few scattered single-point mutations - has twelve completely new nucleotides in a row - a fully formed furin cleavage site that came out of nowhere. There is nowhere else in the genome that COVID differs from BANAL-52 in such a profound way. It’s just BANAL-52 plus a little bit of random mutation plus a fully-formed furin cleavage site that came out of nowhere. Further, the furin cleavage site is weird. It uses the protein arginine twice. But instead of the nucleotides coding for arginine in the usual viral way, both times it uses the codons CGG - the way that higher animals code for arginine. This works fine - it’s just not how viruses do it. So the obvious conclusion is that WIV, which said in 2018 that it was going to find viruses and add furin cleavage sites to them, found a close relative of BANAL-52 and added a furin cleavage site. Since they were humans, and most familiar with the human way of encoding arginine, they added it as CGG both times. COVID seemed surprisingly optimized for infecting humans. Of fifty animals it was tested in, including the usual coronavirus intermediate hosts (pangolins, raccoon-dogs, etc), it was best at infecting human cells. Further, a virus that enters a new species will usually show a burst of mutations as it “figures out” the best way to adapt to that species’ unique biology. But COVID has had a pretty constant mutation rate in humans, from the beginning of the pandemic to the end. That suggests it was already adapted to humans. This could be because the lab screened for viruses with existing adaptations, because they passed it through humanized mice in the lab, or because it adapted in the hundreds of undetected cases that happened between the lab and detection in the wet market. Usually, research with potentially dangerous coronaviruses is done in BSL-3 or 4, ie high to very-high security. But WIV was irresponsibly doing it in BSL-2, ie medium security. The researchers weren’t even required to wear masks. In general, about 1/500 labs will leak any given pathogen they’re working on (?!). But because WIV was researching such an infectious virus in such an irresponsible way, the odds of a leak were much higher. The most likely explanation for all these facts is that WIV went ahead and did the gain-of-function research they said they were going to do (the particular DEFUSE grant proposal we know about got rejected, but it proves that Wuhan wanted to do this, and they could easily have gotten funding somewhere else, or done it out of their regular budget). They found a close relative of BANAL-52 and added a furin cleavage site as a simple twelve-nucleotide insertion, using the human method of encoding arginine that their genetic engineers were familiar with. Then it leaked, spread for a while in the general Wuhan population, and eventually made it to the wet market where it got detected. Peter: As mentioned earlier, the DEFUSE grant was rejected. Further, the grant said that the Wuhan Institute of Virology was responsible for finding the viruses, and the University of North Carolina would do all the gain-of-function research. This was a reasonable division of labor, since UNC was actually good at gain-of-function research, and WIV mostly wasn’t. They had done a few very simple gain-of-function projects before, but weren’t really set up for this particular proposal and were happy to leave it for their American colleagues. Even if WIV did try to create COVID, they couldn’t have. As Yuri said, COVID looks like BANAL-52 plus a furin cleavage site. But WIV didn’t have BANAL-52. It wasn’t discovered until after the COVID pandemic started, when scientists scoured the area for potential COVID relatives. WIV had a more distant COVID relative, RATG-13. But you can’t create COVID from RATG-13; they’re too different. You would need BANAL-52, or some as-yet-undiscovered extremely close relative. WIV had neither. Are we sure they had neither? Yes. Remember, WIV’s whole job was looking for new coronaviruses. They published lists of which ones they had found pretty regularly. They published their last list in mid-2019, just a few months before the pandemic. Although lab leak proponents claimed these lists showed weird discrepancies, this was just their inability to keep names consistent, and all the lists showed basically the same viruses (plus a few extra on the later ones, as they kept discovering more). The lists didn’t include BANAL-52 or any other suitable COVID relatives - only RATG-13, which isn’t close enough to work. Could they have been keeping their discovery of BANAL-52 secret? No. Pre-pandemic, there was nothing interesting about it; our understanding of virology wasn’t good enough to point this out as a potential pandemic candidate. WIV did its gain-of-function research openly and proudly (before the pandemic, gain-of-function wasn’t as unpopular as it is now) so it’s not like they wanted to keep it secret because they might gain-of-function it later. Their lists very clearly showed they had no virus they could create COVID from, and they had no reason to hide it if they did. COVID’s furin cleavage site is admittedly unusual. But it’s unusual in a way that looks natural rather than man-made. Labs don’t usually add furin cleavage sites through nucleotide insertions (they usually mutate what’s already there). On the other hand, viruses get weird insertions of 12+ nucleotides in nature. For example, HKU1 is another emergent Chinese coronavirus that caused a small outbreak of pneumonia in 2004. It had a 15 nucleotide insertion right next to its furin cleavage site. Later strains of COVID got further 12 - 15 nucleotide insertions. Plenty of flus have 12 to 15 nucleotide insertions compared to other earlier flu strains. Sometimes insertions happen because of a mistake in viral replication. Other times the virus gets confused between its own RNA and its host’s, and splices a bit of the host RNA into the virus. This would neatly explain why the insertion used the unusual coding CGG for arginine, which is common in animals but rare in viruses. On the other hand, it’s not that rare in viruses - COVID uses CGG for arginine about 3% of the time. And human engineers don’t necessarily use it any more than that - Peter was able to find one example of humans adding arginine to a virus, and 0 out of the 5 arginines added were CGG. COVID’s furin cleavage site is a mess. When humans are inserting furin cleavage sites into viruses for gain-of-function, the standard practice is RRKR, a very nice and simple furin cleavage site which works well. COVID uses PRRAR, a bizarre furin cleavage site which no human has ever used before, and which virologists expected to work poorly. They later found that an adjacent part of COVID’s genome twisted the protein in an unusual way that allowed PRRAR to be a viable furin cleavage site, but this discovery took a lot of computer power, and was only made after COVID became important. The Wuhan virologists supposedly doing gain-of-function research on COVID shouldn’t have known this would work. Why didn’t they just use the standard RRKR site, which would have worked better? Everyone thinks it works better! Even the virus eventually decided it worked better - sometime during the course of the pandemic, it mutated away from its weird PRRAR furin cleavage site towards a more normal form. Further, COVID’s furin cleavage site was inserted via what seems to be a frameshift mutation - it wasn’t a clean insertion of the amino acids that formed the site, it was an insertion of a sequence which changed the context of the surrounding nucleotides into the amino acids that formed the site. This is a pointless too-clever-by-half “flourish” that there would be no reason for a human engineer to do. But it’s exactly the kind of weird thing that happens in the random chance of evolution. COVID is hard to culture. If you culture it in most standard media or animals, it will quickly develop characteristic mutations. But the original Wuhan strains didn’t have these mutations. The only ways to culture it without mutations are in human airway cells, or (apparently) in live raccoon-dogs. Getting human airway cells requires a donor (ie someone who donates their body to science), and Wuhan had never done this before (it was one of the technologies only used at the superior North Carolina site). As for raccoon-dogs, it sure does seems suspicious that the virus is already suited to them. The claim that COVID is uniquely adapted to humans is false. The paper that claimed that defined how well COVID was adapted to different animals by those animals’ difference (on the relevant cell receptors) from humans. So in its methodology, humans came out #1 by default. If you don’t do that, COVID is better-adapted to many other animals. It’s not necessarily true that viruses see a burst of mutations when they enter a new host. COVID spread to deer and mink, and in neither case was there a burst of mutations. COVID has a pretty simple job of infecting respiratory cells and is already very good at it, regardless of species. In Yuri’s model, Wuhan Institute of Virology picked up a discarded grant and decided to do the gain-of-function half allotted to a different university, despite their relative inexperience. They skipped over all the SARS-like viruses they were supposed to work on, and all the standard gain-of-function model backbones, in favor of BANAL-52, a virus which would not be discovered for another two years, but which they somehow had samples of, which they had for some reason decided to keep secret despite its total lack of interestingness. Then they would have had to eschew all usual gain-of-function practices in favor of inserting a weird furin cleavage site that shouldn’t have worked according to the theory they had at the time, via a frameshift mutation. Then they would have had to culture it, a technique beyond their limited capabilities. Then it would have had to leak, and magically show up again in front of the raccoon-dog stall at a wet market. Yuri: WIV wouldn’t have needed to keep BANAL-52 “secret” in some kind of sinister way. Plenty of researchers have backlogs of work they haven’t published yet. Probably they a found BANAL relative in one of their normal sampling trips, did some preliminary studies on it, and planned to publish it later once they cleaned up their data. Everyone works like this. The part of DEFUSE saying that they would only work on viruses that were 95% similar to SARS is unclear and might mean something else. It looks more like they say they’ll start with those viruses, but also do some work on novel viruses. BANAL-52 could have been one of the novel viruses. The furin cleavage site is weird, but the researchers might have done that on purpose, to make the virus easier to keep track of, or to test different furin cleavage sites. Depending on the exact BANAL-52 relative they used, it might not even be a frameshift; there’s a particular way to spell serine that would make the insertion more natural. The claims that COVID can’t be cultured in normal media are based on speculative original research by Peter and might not hold up. Peter: WIV did most of its virus-gathering in a trip to a Yunnan cave between 2010 and 2015. All those viruses have long since been processed and added to the database. There’s no sign that they made more trips to Yunnan caves, and no reason for them to keep that secret. So the idea that they might just have some new viruses they didn’t publish doesn’t hold up. But suppose they did make more trips. Given the amount of time between the DEFUSE proposal and COVID, if they kept to their normal virus-collection rate, they would have gotten about thirty new viruses. What’s the chance that one of those was BANAL-52? There are thousands of bat viruses, and BANAL-52 is so rare that it wasn’t found until well after the pandemic started and people were looking for it very hard. So the chance that one of their 30 would be BANAL-52 is low. Also, they said in DEFUSE that they planned to go back to the same Yunnan cave. But BANAL-52 was found far away from that cave, so unless it ranged over a wide area, they probably couldn’t have found it even if they got very lucky. Session 3: Closing Arguments This third debate was supposed to be about “inference”, ie how much Bayesian evidence was provided by each of the facts given so far, and how to fit them into the Rootclaim probabilistic model. I’m going to relegate my summary of the more probabilistic half to the next section of this post, and just include the closing arguments here. Saar: Peter’s case hinges on the idea that it’s very improbable that a lab leak pandemic would first show up at a wet market. But this isn’t necessarily improbable. The Huanan Seafood Market had several factors that made it a likely location for a superspreader event. It was busy, with over 10,000 visitors a day. Many of the people there (eg the 1,000 vendors) came back daily, letting them reinfect each other. It had poor ventilation, especially in the high-positivity area near the raccoon-dog stall. It had cold wet surfaces on which the virus could survive for long periods. It was indoors, which prevented UV light from killing the virus. Given a small amount of sporadic COVID going around Wuhan, it’s not surprising for the first place it started spreading en masse to be a wet market. In fact, we have several examples of this. When China was COVID Zero, there would occasionally be small outbreaks that the authorities would have to contain. Most of these were at wet markets. For example, the big COVID outbreak in Beijing started at Xinfadi Market, their local seafood market. This couldn’t be an animal spillover, because there were no raccoon-dogs or other weird wildlife there. So it must be that wet markets are natural places for superspreader events. There are several other examples, which make up about half of the total outbreaks in Zero COVID era China, plus others in Singapore and Thailand. Since COVID clusters concentrate in wet markets even when there is no animal spillover, we should accept this as a property of the virus, and not attribute any significance to the fact that this happened in Wuhan too. Peter: About 1/10,000 citizens of Wuhan was a wet market vendor. So there’s a 1/10,000 chance that the first known COVID case should be a wet market vendor by chance alone. Weibo lists the most popular places for people to check in to their network on their phones, and the wet market was the 1600th most popular place in Wuhan, meaning that if you weight locations by busy-ness, there’s a less than 1/1600 chance that the first cases would be in the wet market. Yes, the wet market is indoors, has mediocre ventilation, has repeat visitors, etc. So do thousands of other places in Wuhan, like schools, hospitals, workplaces, places of worship. The wet market isn’t special in any way. And again, it wasn’t a superspreader event! COVID spread at the same rate in the wet market as it does everywhere else: doubling once per 3.5 days. It doesn’t matter what kinds of arguments you can come up with for why the wet market should have been the perfect superspreader event location, we can look at it and see that it wasn’t. It’s an environment that spreads COVID at exactly the normal rate. Zero COVID era Chinese outbreaks were concentrated in wet markets because they received infected animal products. We know why there was an outbreak in the Xinfadi Market in Beijing: it was because the seafood stall got frozen fish from some non-Zero-COVID country, the fish had COVID particles on it, and the vendor got infected and spread it to everyone else. Something like this is true for the other Chinese wet market based outbreaks we know about it. So this makes the opposite point you think it does: wet markets start outbreaks because there are infected goods being sold there. Then the virus spreads through the wet market at a completely normal rate. Saar: The Weibo list of 1600 places bigger than the wet market is likely inaccurate, because it's based on check-in data and people don't check in to seafood markets. Most of those 1600 places aren't amenable to superspread. The 70 markets supposedly bigger than Huanan are irrelevant, because they're supermarkets, open air markets, etc. Huanan is the largest seafood market in central China, and a more likely place for the first cluster of cases to be noticed. Markets weren't a common spillover location in SARS1, so the zoonosis hypothesis hasn't "called" this event in a way that should give them a high Bayes factor. And there’s still plenty of evidence for isolated (though not super-spreading) pre-market cases. A British expatriate in Wuhan, Connor Reed, says he got sick in November, three weeks before the first wet market case. Later the hospital tested his samples and said it was COVID. Another paper reports 90 cases before the first wet market one. Peter: Connor Reed was lying. The case wasn’t reported in any peer-reviewed paper. It was reported in the tabloid The Daily Mail, months after it supposedly happened. He also told the Mail that his cat died of coronavirus too, which is rare-to-impossible. Also, to get a positive hospital test, he would have had to go to the hospital, but he was 25 years old and almost no 25-year-olds go to the hospital for coronavirus. His only evidence that it was COVID was that two months later, the hospital supposedly “notified” him that it was. The hospital never informed anyone else of this extremely surprising fact which would be the biggest scientific story of the year if true. So probably he was lying. Incidentally, he died of a drug overdose shortly after giving the Mail that story; while not all drug addicts are liars, given all the other implausibilities in his story, this certainly doesn’t make him seem more credible. And in any case, he claimed he got his case at a market “like in the media” The other 90 cases are also fake. A lab leak guy found a paper that mentioned 90 more cases than other papers, and made up a conspiracy theory where the author was trying to secretly communicate that there had been 90 secret cases before any of the confirmed cases, even though there was nothing about this in the text of the paper. But actually that paper just counted cases differently than other papers, and they were referring to normal cases after the pandemic officially started. Again, I’ll come back to the discussion about inference later, but for now, here’s a table of both sides’ reasoning. This exact presentation comparing both analyses is mine3, but you can see Saar’s version here, and Peter’s starting at 45:33 of this video. Slightly made up; the two sides didn’t express their probabilities in the same way and I had to make editorial decisions to match them. Note that these aren't entirely comparable because Peter is being laxer about out-of-model probability than Saar. Although Saar's final odds here are 533-to-1, this just the central estimate. Rootclaim’s real final probability is 94% lab leak. You can see their analysis here. And The Winner Is . . . … … … … … Peter and the zoonosis hypothesis. This was a decisive victory. There were two judges, who each gave separate verdicts (or were allowed to declare a draw). Both judges decided in favor of Peter. You can see the judges’ own summary of their reasoning here (Will, Eric) Manifold agreed with the judges. There was a prediction market on who would win. It started out 70-30 in favor of lab leak. As the videos came out, zoonosis started doing better and better. I don’t want to take the exact final numbers too seriously, since I think some of the later price increases involved hints from the participants’ behavior. But it’s clear which way viewers thought the wind was blowing4. Around the same time, the Good Judgment Project - Philip Tetlock’s group studying superforecasters - put out a report on the lab leak hypothesis. After studying it in depth, his forecasters ended up 75-25 in favor of zoonosis. The Rootclaim debate was one of ten sources they said they found especially interesting. And also around the same time, and unrelated to any of this, the Global Catastrophic Risks Institute surveyed experts (“168 virologists, infectious disease epidemiologists, and other scientists from 47 countries”) and found the same thing (though see here for some potential problems with the survey): For what it’s worth, I was close to 50-50 before the debate, and now I’m 90-10 in favor of zoonosis. III. The Math And The Aftermath The third debate session was about “inference”, how to put evidence together. I put this part off until after disclosing the winner, because I wanted to talk about some of these issues at more length. The Math: Judges Both judges included a probabilistic analysis in their written decision. Here’s the same table as above, expanded to add the judges: I shoehorned the judges’ factors into the categories I already had; some of them were actually subtly different from Peter’s, Saar’s, and each other’s. The “priors” category is especially a mess here. We’ll go over these later, but I get the impression that they both thought of probabilistic analyses as an afterthought. For example, Judge Eric wrote 30,000 words about which considerations moved him, and only then includes the analysis, saying: I am not convinced that this Bayesian calculation is even an appropriate way to estimate the relative posterior probability of Z and LL; it just seemed fair that after criticizing Rootclaim’s calculations at length I should make an attempt at it myself. Judge Will’s decision ran to 10,000 words. He said he independently tried both reasoning it out intuitively, and running the Bayesian analysis, and was relieved when these two methods returned the same result. He said: I am skeptical that the Bayesian decision making/evaluation methods are any more "objective" than [intuitive reasoning]. I think they maximize legibility, not objectivity, and tend to hide the intuitive/heuristic portion in the data inclusion step and values, where it’s harder to see . . . I am not skilled in the Bayesian method, and I am sure I made significant mistakes. More time and practice would improve and refine my estimates. At the fundamental rules of the universe level, Bayesian analysis must be the best way to evaluate evidence. However, I am unsure that it’s a good strategy for a human given our cognitive limitations, and doubly unsure it’s truly being used (in the dispassionate sense) where the outcome is social desirability/fame/Twitter likes. I’m focusing on this because Saar’s opinion is that the debate went wrong (for his side) because he didn’t realize the judges were going to use Bayesian math, they did the math wrong (because Saar hadn’t done enough work explaining how to do it right), and so they got the wrong answer. I want to discuss the math errors he thinks the judges made, but this discussion would be incomplete without mentioning that the judges themselves say the numbers were only a supplement for their intuitive reasoning. That having been said, let’s look deeper into some of Saar’s concerns. The Math: Extreme Odds Saar complained that Peter’s odds were too extreme. For example, Peter said there was only a 1/10,000 chance that a lab leak pandemic would first show up at a wet market. Peter’s argument went something like: obviously a zoonotic pandemic would start at a site selling weird animals. But a lab leak pandemic - if it didn’t start at the lab - could show up anywhere. 1/10,000 Wuhan citizens work at the wet market. So if a lab leak was going to show up somewhere random, the wet market was a 1/10,000 chance. Saar had specific arguments against this, but he also had a more general argument: you should rarely see odds like 1/10,000 outside of well-understood domains. In his blog post, he gave this example: A prosecutor shows the court a statistical analysis of which DNA markers matched the defendant and their prevalence, arriving at a 1E-9 probability they would all match a random person, implying a Bayes factor near 1E9 for guilty. But if we try to estimate p(DNA|~guilty) by truly assuming innocence, it is immediately evident how ridiculous it is to claim only 1 out of a billion innocent suspects will have a DNA match to the crime scene. There are obviously far better explanations like a lab mistake, framing, an object of the suspect being brought by someone to the scene, etc. So the real p(wet market|lab leak) isn’t the 1/10,000 chance a pandemic arising in a random place hits the wet market, but the (higher?) probability that there’s something wrong with Peter’s argument. Then Saar tried to show specific things that might be wrong with Peter’s argument. I didn’t find his specific examples convincing. But maybe the question shouldn’t be whether I agreed with him. It should be whether I’m so confident he’s wrong that I would give it 10,000-to-1 odds. This makes total sense, it’s absolutely true, and I want to be really, really careful with it. If you take this kind of reasoning too far, you can convince yourself that the sun won’t rise tomorrow morning. All you have to do is propose 100 different reasons the sunrise might not happen. For example: The sun might go nova.
Inline links: Pekar 2022, Pipes 2021, says, 3, here, this video, https://substackcdn.com/image/fetch/$s_!8aU2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F815fe32d-d7ea-401b-b3a2-d8cd25b52ee8_490x780.png, https://substackcdn.com/image/fetch/$s_!0Tm_!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd0492f69-7b7e-4611-9d76-64ef8d7f59d5_511x511.png, Will, Eric, agreed, 4, put out a report on the lab leak hypothesis, https://substackcdn.com/image/fetch/$s_!g7k2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F37f1b493-b556-41ec-925e-03f9d8bc26cb_1456x849.webp, surveyed experts, see here, https://substackcdn.com/image/fetch/$s_!Zejl!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe9c88e87-b6ca-4c6d-840e-24da726f50b7_975x365.png, https://substackcdn.com/image/fetch/$s_!T5rV!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4983e2cd-4151-42de-9685-08037ef7a8e8_635x788.png
Okay, this one is just awful. It takes the risky gambit above - giving extreme odds to something - then doubles down on it by multiplying across twenty different stages to get a stupendously low probability of 1/5*10^25. If we believe this, it’s more likely that we win the lottery three times in a row than that we learn lab leak was true after all. Eliezer Yudkowsky calls this the Multiple Stage Fallacy. Even aside from the failure mode in the sunrise example above (where people are too reluctant to give strong probabilities), it fails because people don’t think enough about the correlations between stages. For example, maybe there’s only 1/10 odds that the Wuhan scientists would choose the suboptimal RRAR furin cleavage site. And maybe there’s only 1/20 odds that they would add a proline in front to make it PRRAR. But are these really two separate forms of weirdness, such that we can multiply them together and get 1/200? Or are scientists who do one weird thing with a furin cleavage site more likely to do another? Mightn’t they be pursuing some general strategy of testing weird furin cleavage sites? (For example, Yuri proposed that, because the scientists wanted to understand how pandemic coronaviruses originate in nature, they might deliberately pick more natural-looking features over more designed-looking ones, which would neatly explain many features seemingly inconsistent with lab leak. Is this a conspiracy theory? Rootclaim is able to successfully route around this question. If the probability of a feature happening in nature is X, then the probability of it happening in this variant of lab leak scenario is X * [chance that the scientists wanted to imitate nature). This gives it a (deserved) complexity penalty without ruling out this (non-zero and potentially important) possibility.) In any case, Peter didn’t care as much about probabilistic analysis as Saar, he didn’t make his case hinge on this slide, and he might have been kind of using it to troll Rootclaim (which definitely worked). He might not have been making any of the mistakes above. But anyone who took this slide seriously would end up dramatically miscalibrated. The Math: Big Pictures Another of Saar’s concerns with the verdict was that Peter was an extraordinary debater, to the point where it could have overwhelmed the signal from the evidence. It’s hard to watch the videos and not come away impressed. Peter seems to have a photographic memory for every detail of every study he’s ever read. He has some kind of 3D model in his brain of Wuhan, the wet market, and how all of its ventilation ducts and drains interacted with each other. Whenever someone challenged one of his points, he had a ten-slide PowerPoint presentation already made up to address that particular challenge, and would go over it with complete fluency, like he was reciting a memorized speech. I sometimes get accused of overdoing things, but I can’t imagine how many mutations it would take to make me even a fraction as competent as Peter was. Saar’s closing argument included the admission: Peter, I think everyone can agree, has much more knowledge on [COVID] origins than we do. He's invested much more time. He may be a much more talented researcher. He's much more into the details. He probably knows the best in the world on origins at this point. Once you’ve described your opponent that way in your closing argument, what’s left of your case? Saar thought a lot was left. Throughout the debate, he tried to make a point about how getting the inference right was more important than winning sub-sub-sub-debates about individual lines of evidence. Although Peter won most specific points of contention, Saar thought that if the judges could just keep their mind on the big picture, they would realize a lab leak was more likely. I’m potentially sympathetic to arguments like Saar’s. Imagine a debate about UFOs. Imaginary-Saar says “UFOs can’t be real, because it doesn’t make sense for aliens to come to Earth, circle around a few fields in Kansas, then leave without providing any other evidence of their existence.” Imaginary-Peter says “John Smith of Topeka saw a UFO at 4:52 PM on 6/12/2010, and everyone agrees he’s an honorable person who wouldn’t lie, so what’s your explanation of that?” Saar says “I don’t know, maybe he was drunk or something?” Peter says “Ha, I’ve hacked his cell phone records and geolocated him to coordinates XYZ, which is a mosque. My analysis finds that he’s there on 99.5% of Islamic holy days, which proves he’s a very religious Muslim. And religious Muslims don’t drink! Your argument is invalid!” On the one hand, imaginary-Peter is very impressive and sure did shoot down Saar’s point. On the other, imaginary-Saar never really claimed to have a great explanation for this particular UFO sighting, and his argument doesn’t depend on it. Instead of debating whether Smith could or couldn’t have been drunk, we need to zoom out and realize that the aliens explanation makes no sense. The problem was, Saar couldn’t effectively communicate what his big picture was. Neither deployed some kind of amazingly elegant prior. They both used the same kind of evidence. The only difference was that Peter’s evidence hung together, and Saar’s evidence fell apart on cross-examination. I think - not because Saar really explained it, but just reading between the lines - Saar thought the un-ignorable big picture evidence was the origin in a city with a coronavirus gain-of-function lab, and the twelve-nucleotide insertion in the furin cleavage site. To some degree, Peter just ate the loss on those questions. No matter how you slice it, it really is a weird coincidence that the epidemic started so close to Asia’s biggest coronavirus laboratory. Peter tried to deflect this - he pointed out there were other BSL-3 and BSL-4 laboratories in Beijing, Shanghai, Shenzhen, etc. But this was a rare question where he unambiguously came out looking worse - the other cities’ labs had much less coronavirus-specific research. Wuhan really was unique (aside from the other big coronavirus lab in North Carolina). Peter did better when he tried to control the damage: there are a couple hundred million people in the South Asian areas where people eat weird animals exposed to virus-infected bats, Wuhan has a population of about 12 million, so maybe 1.5% of all potential zoonotic pandemics should start in Wuhan. Peter tried to argue that Wuhan was a local trade center, so maybe we should up that to 5 - 10%. 5 - 10% coincidences aren’t that rare. Even 1.5% coincidences happen sometimes. Likewise, the furin cleavage site really does stand on a genetic map. I didn’t feel like either side did much math to quantify how weird it was. Naively, I might think of this as “30,000 bases in COVID, only one insertion, it’s in what’s obviously the most interesting place - sounds like 30,000-to-one odds against”. Against that, a virus with a boring insertion would never have become a pandemic, so maybe you need to multiply this by however much viral evolution is going on in weird caves in Laos, and then you would get the odds that at least one virus would have an insertion interesting enough to go global. Neither participant calculated this in a way that satisfied me (though see here for related discussion). Instead, Peter tried to undermine the furin argument by showing that, as surprising as the site was under a natural origin, it would be an even more surprising choice for human engineers. Saar argued it wasn’t - but because of his policy of giving adjusted-for-model-error odds, he only gave this a factor of 30 in his analysis. Since Peter gave it a higher factor of 50 in his analysis, it looked from the outside like Saar had already conceded this point, and the judges were mostly happy to go with Saar’s artificially-low estimate. The Math: Double Coincidences Saar brought up an interesting point halfway through the debate: you should rarely see high Bayes factors on both sides of an argument. That is, suppose you accept that there’s only a 1-in-10,000 chance that the pandemic starts at a wet market under lab leak. And suppose you accept there’s only a 1-in-10,000 chance that COVID’s furin cleavage site could evolve naturally. If lab leak is true, then you might find 1-in-10,000 evidence for lab leak. But it’s a freak coincidence that there was 1-in-10,000 evidence for zoonosis5. Likewise, if zoonosis is true, you might find 1-in-10,000 evidence for this true thing. But it’s a freak coincidence that there was 1-in-10,000 evidence for lab leak. Either way, you’re accepting that a 1-in-10,000 freak coincidence happened. Isn’t it more likely you’ve bungled your analysis? I was following along at home, and I definitely bungled this point; I had some high Bayes factors on both sides. I adjusted some of them downward based on Saar’s good point, but how far should we take it? Here I remember The Pyramid And The Garden: you can get very strong coincidences if you have many degrees of freedom, ie buy a lot of lottery tickets. So for example, suppose there are fifty things about a virus. You should expect at least one of those to have a one-in-fifty coincidence by pure chance. What about more than that? You might be able to get away with this by saying there are an infinite number of possible conspiracy theories, and some from that infinite set are brought into existence when a strong enough coincidence makes them plausible. For example, it’s really weird that John Adams and Thomas Jefferson both died on the 50th anniversary of the Declaration of Independence. If I wanted, I could form a conspiracy theory about a group of weird assassins obsessed with killing Founding Fathers on important dates, and then Jefferson and Adams’ deaths would be 1/10,000 evidence for that theory. But this is the Texas Sharpshooter Fallacy, which Saar warned against several times. I don’t know if “the virus started in Wuhan, which is where they’re doing this research” gets a Texas Sharpshooter penalty, or how high that penalty should be. But the furin cleavage site doesn’t - people were talking about lab leak before anyone noticed it. The Aftermath: Peter Peter seemed satisfied with the result, in an understated sort of way: It seemed like an interesting experiment in monetizing the debunking of a conspiracy theory. I think there's usually a big asymmetry where it's easy to get rich spreading bullshit (like, the top anti-vaxxers during the pandemic all made a million dollars a year on substack), but it's almost impossible to make money on debunking it. The Rootclaim challenge seemed like one rare case where the opposite was true. Beyond that, I don't know what it's good for. It does seem like there could be a positive social impact from more people understanding that the lab leak hypothesis is (almost certainly) false. The Aftermath: Saar Saar says the debate didn’t change his mind. In fact, by the end of the debate, Rootclaim released an updated analysis that placed an even higher probability on lab leak than when they started. In his blog post, he discussed the issues above, and said the judges had erred in not considering them. He respects the judges, he appreciates their efforts, he just thinks they got it wrong. Although he respected their decision, he wanted the judges to correct what he saw as mistakes in their published statements, which delayed the public verdict and which which Viewers Like You did not appreciate: I ran an early draft of this post by him. There was some miscommunication about the exact publication date, so he hasn’t had time to write up a full response, but he has some quick thoughts (and I’ll link the full response when he writes it). He says: We will provide a full response to this post soon, but the main problem with it is fairly simple: There is general agreement that the main evidence for zoonosis is HSM (Huanan Seafood Market) forming an early cluster of cases. The contention is whether it is amazing 10,000x evidence, or is it negligible. All other evidence points to a lab leak, and if HSM is shown to be weak, lab leak is a clear winner. We provided an analysis of why it is negligible that is as close to mathematical proof as such things can be. Read it here. Scott and I exchanged a few emails on this issue and Scott preferred to discuss more intuitive analyses of HSM, using rules of thumb that likely served him well in the past. While I believe I managed to mostly explain where these failed, and Scott understands HSM is far weaker evidence than he initially thought6, he still has a very strong intuitive feeling (based on years of dealing with probabilities) that this is some exceptional coincidence, and that prevents him from properly updating his posterior. At the end of the day, this cannot be settled without going through our semi-formal derivation, understanding it, and either identifying the problem with it or accepting it (and thereby accepting lab-leak to be more likely). Here is a quick summary of the mistakes made by those claiming HSM is strong evidence: The first mistake is conflating Bayes factors with conditional probabilities. 1/10000 is the supposed conditional probability p(HSM|Lab Leak), That should be divided by the conditional probability of HSM under Zoonosis. Markets were not identified as a high-risk location prior to this outbreak (This will be elaborated in the full response), and in SARS1 the spillovers were mostly at restaurants and other food handlers that deal more closely with wildlife. While it's cool to point to the raccoon dog photo, that was a result of a retrospective search (we don't know what other photos they took which in retrospect would be brought up as premonition). Unbiased data shows markets are not a likely spillover location for zoonosis. We originally estimated p(HSM|Zoonosis)<0.1. Following more research we did to answer Scott's questions, this is more likely <0.03.
Inline links: Multiple Stage Fallacy, see here, 5, The Pyramid And The Garden, Texas Sharpshooter Fallacy, blog post, 6
Spring Meetups Everywhere 2024
March 30, 2024 · Original source
ASHEVILLE, NORTH CAROLINA, USA Contact: Vicki Williams Contact Info: Vickirwilliams[at]gmail[dot]com Time: Saturday, April 27th, 6:00 PM Location: Biltmore Lake Fire Pit, 80 Lake Dr. Candler, NC. Parking in front of the basketball court, then walk along the lake to the fire pit behind the tennis court. Coordinates: https://plus.codes/867VG8MW+9G Notes: Please RSVP so I can get in touch in case of change in plans.
Inline links: https://plus.codes/867VG8MW+9G
GREENSBORO, NORTH CAROLINA, USA Contact: Randall Hayes Contact Info: vsi[dot]beacon[at]gmail[dot]com Time: Saturday, April 6th, 5:00 PM Location: Old Town Draught House, 1205 Spring Garden St, Greensboro, NC 27403 Coordinates: https://plus.codes/8782358Q+7P Notes: This is a place of business, so no outside food or drink. Sorry. https://oldtowndraught.com/ If you're interested in Sci-Fi, there's a con going on down the block!
Inline links: https://plus.codes/8782358Q+7P
RALEIGH-DURHAM, NORTH CAROLINA, USA Contact: Logan Contact Info: Logan[dot]the[dot]word[at]gmail[dot]com Time: Saturday, May 11th, 1:00 PM Location: Ponysaurus Brewing Co (219 Hood St, Durham). We'll be at the outdoor seating area with an ACX sign on the table Coordinates: https://plus.codes/8773X4Q3+QW Group Link: https://groups.google.com/g/rtlw Notes: There will be pizza! The venue serves beer but is kid-friendly. I'll have more details on the Google group (see link)
Inline links: https://plus.codes/8773X4Q3+QW, https://groups.google.com/g/rtlw
Your Book Review: Dominion
June 28, 2024 · Original source
Scully goes to a hog farm in North Carolina owned by one of the world’s largest pork producers, Smithfield Foods, to see for himself what we lose when we treat animals like literal production machines. He uses his conservative credentials to slide under their radar. He’s given a full tour of the farm, access that other animal activists can only achieve by breaking in under cover of night.
Meetups Everywhere 2024: Times & Places
August 29, 2024 · Original source
(See “Manhattan, New York” or “Brooklyn, New York”) North Carolina ASHEVILLE, NORTH CAROLINA, USA Contact: Vicki Williams Contact Info: vickirwilliams[at]gmail[dot]com Time: Friday, September 27th, 06:00 PM Location: Biltmore Lake (aka Enka Lake) Fire Pit. Park near 88 Lake Dr. in Candler. Follow the path by the lake to the fire pit behind the tennis courts. Coordinates: https://plus.codes/867VG8MW+9G Notes: Please RSVP for details, meal planning, and rain location.
Inline links: https://plus.codes/867VG8MW+9G
Contact: Vicki Williams Contact Info: vickirwilliams[at]gmail[dot]com Time: Friday, September 27th, 06:00 PM Location: Biltmore Lake (aka Enka Lake) Fire Pit. Park near 88 Lake Dr. in Candler. Follow the path by the lake to the fire pit behind the tennis courts. Coordinates: https://plus.codes/867VG8MW+9G Notes: Please RSVP for details, meal planning, and rain location. RALEIGH-DURHAM, NORTH CAROLINA, USA Contact: Logan Contact Info: Logan[dot]the[dot]word[at]gmail[dot]com Time: Saturday, September 28th, 02:00 PM Location: Ponysaurus Brewing Co (219 Hood St, Durham). We'll be at the outdoor seating area with an ACX sign on the table Coordinates: https://plus.codes/8773X4Q3+QW Group Link: https://groups.google.com/g/rtlw Notes: There will be pizza! The venue serves beer but is kid-friendly. I'll have more details on the Google group (see link)
Inline links: https://plus.codes/867VG8MW+9G, https://plus.codes/8773X4Q3+QW, https://groups.google.com/g/rtlw
Meetups Everywhere Spring 2025: Times & Places
March 25, 2025 · Original source
(See “Manhattan, New York” or “Brooklyn, New York”) North Carolina ASHEVILLE Contact: Vicki Williams Contact Info: vickirwilliams[a t]gmail[period]com Time: Friday, April 04th, 06:00 PM Location: Biltmore (aka Enka) Lake fire pit. Park near 88 Lake Dr., Candler. Walk along the trail by the lake to the fire pit behind the tennis courts. Coordinates: https://plus.codes/867VG8MW+9G Notes: Please RSVP so you can be notified in case of change in plans. Kid friendly (there's a playground nearby) and pet friendly (leashed please). We'll have a campfire and some fire appropriate food stuffs.
Inline links: https://plus.codes/867VG8MW+9G
Your Review: Alpha School
June 27, 2025 · Original source
NextGen Academy (Austin) —Perhaps the most radical experiment. Afternoons are spent training in competitive esports & game design. Each new campus launched with <10 students, two or more local guides, and the same two‑hour core. Simultaneously Alpha opened a Miami elementary campus, promoted the idea that cities could launch “micro schools” if they had enough local demand (unless you count Miami, none actually launched) and piloted a beta-test of a Home‑School version of the platform. Early homeschool data showed that kids were using it for ~2 hours/day as planned, but only seeing a 1x learning growth — still a fine result for only doing 2-hours of academics per day, but a long way from what Alpha was delivering on their own campuses, so the program has stayed in beta. Jan 2025 | Charter & Licence Play Alpha now had a parent company, “2-hour Learning”, which sat above all of the schools, the home school product, and the platform itself (that they now offer to license out to third parties). The parent company filed under “Unbound Academy” to launch charter schools in Arizona and Pennsylvania. The Pennsylvania school was rejected, but the Arizona school will launch in fall 2025. There are more applications pending in at least Utah, Arkansa, North Carolina, South Carolina (and likely more). While the PR spin around these schools is “AI-driven, no teachers” in practice they use 20:1 teacher guide:student ratios (vs the 5:1 ratio at the Alpha private schools) Generally states subsidize charter schools in the neighborhood of $10,000 per student – which is a lot lower than what Alpha charges. They should be able to make those economics work by using fewer, less expensive teachers, not having an expensive campus (or no campus at all for the online schools), skimming on the extras (no trips to Poland), avoiding teaching the youngest kids (Arizona is 4th-8th grade), and being willing to accept smaller or even negligible margin on their learning platform. The goal of these schools does not seem to be making money or profit – at least not right away. The goal seems to be rapidly expanding the program to have more influence, and to see if they can make it work with “non-selected kids at a low price point”. Fall 2025 and Beyond | The Future The Alpha website claims the following locations are launching in Fall 2025: Houston, TX
Inline links: NextGen Academy, 2-hour Learning, locations
Meetups Everywhere 2025: Times and Places
August 29, 2025 · Original source
Contact: Bryce Contact Info: bryce[a t]brycedav[period]is Time: Wednesday, September 24th, 06:30 PM Location: Java's Cafe (16 Gibbs St) Coordinates: https://plus.codes/87M4594X+W9 North Carolina ASHEVILLE Contact: Vicki Williams Contact Info: vickirwilliams[a t]gmail[period]com Time: Friday, September 26th, 6:00 PM Location: Biltmore Lake (aka Enka Lake) Fire Pit behind the ball courts. Google maps '420 Lake Dr, Candler, NC 28715' Coordinates: https://plus.codes/867VG8MW+9G Notes: Please RSVP for reminder and in case of reschedule. Kids welcome (there's a nearby playground) as are leashed pets. There will be a campfire and typical fire related food stuffs.
Inline links: https://plus.codes/87M4594X+W9, https://plus.codes/867VG8MW+9G
Meetups Everywhere Spring 2026: Times & Places
April 01, 2026 · Original source
Contact: Matt Contact Info: matt[.]faherty530[@]gmail[.]com Time: Sunday, April 5th, 2:00 PM Location: Crafted Kup Coordinates: https://plus.codes/87H8M3VX+3Q Notes: First time hosting, so please email me if you’re planning on attending. North Carolina ASHEVILLE Contact: Vicki Contact Info: vickirwilliams[@]gmail[.]com Time: Friday, May 15th, 6:00 PM Location: Biltmore Lake Community. Please RSVP for details. Coordinates: https://plus.codes/867VG8MV+6W Notes: Exact location is weather dependent so please RSVP. Kids are welcome. Dogs may be welcome (if we meet outside). Some food is provided, but also some people usually contribute. It is up to you. RSVPs are helpful in planning that too.
Inline links: https://plus.codes/87H8M3VX+3Q, https://plus.codes/867VG8MV+6W