Bibliographic record
Abstract
A Theory of History Nathan Curtis Roberts (bio) Explore, expand, exploit, exterminate. This was how Oliver Hall passed his time during quarantine. He'd fallen off the wagon with unexpected force, and he'd been off it for eighteen months, living in his late father's unlivable hovel in South Salt Lake, surrounded by empty bottles, aggregating 520 days of headache and heartburn and hangover. He started drinking to mourn the end of his friendship—and working relationship—with Daniel Erlich, a famous artist and filmmaker. He continued drinking because that's how it usually went with drunks, though this was a special kind of bender, the kind you don't expect to come back from. In March of that year, pandemic arrived as the grim late-winter snow melted. And earthquakes shook Utah. And Oliver felt the same uncertainty everyone everywhere was feeling. But being a contrarian—one who didn't believe the universe had any guiding intelligence in its design but did think it had a darkly hilarious wit—he decided to sober up. While all the other drinkers in that largely dry state stood in three-block-long social-distanced lines to purchase enough booze to last until the end of days, Oliver Hall abstained. He knew what to expect. The days of shaking, the nights of sleeplessness, the weeks of startling at every unexpected noise. He searched the app store for a diversion to occupy him through the worst of it. And this was what he found: explore, expand, exploit, exterminate. It was a game about conquering neighbors, trading and hoarding resources, and doing puzzles that weren't really puzzles so much as lining up colors and watching them cascade randomly to the bottom of the screen. He could watch the cascading colors for hours on end. But sometimes he watched YouTube videos. He was especially interested in watching triathletes do their sports and discuss their training. There was a Canadian who was popular on the platform, himself a recovered addict; his upper body was lean almost to the point of scrawniness, and his ass was a perfect muscular globe; he had a movie star chin and jaw, which fit awkwardly but appealingly with his goofy smile; and this triathlete had remarkable endurance not only for athleticism, but for looking into a camera and describing his regimen minute by minute, number by number, week after week. It was as inspiring as it was lulling, and it got Oliver to start running again. And there was Twitter. He didn't participate, but he observed. For no good reason—certainly no reason he could have explained—he kept separate accounts for every interest. One for following visual artists, one for politics, and one for witty gays [End Page 20] who lived for the rare occasions when someone compared them to Oscar Wilde. This was how Oliver spent his pandemic. He went to a behavioral health clinic where everyone was well-intentioned and no one was well-credentialed. (This suited Oliver: for multiple myeloma he would have preferred well-credentialed, but for addiction and major depressive disorder, well-intentioned were where it was at.) He ran and cycled. He played his phone game. He did a lot of "doomscrolling," as everyone on the platform was calling it. And that was how he discovered Simon Abeles, a labor economist with a sense of humor and a handsome man for a profile picture. Simon started showing up in all three of Oliver's feeds—art, politics, gays. He was intimidatingly smart, a genuine polymath, as knowledgeable as if he had a team of researchers just for tweeting. His jokes were a little effortful, and they occurred at clockwork intervals, as if he had them scheduled so that he would occasionally seem human. But he spent most of his time excoriating his enemies in 280-character salvos, fighting with strangers, and calling his more conservative colleagues world-class idiots. Simon Abeles was brilliant, he was beautiful, and he was belligerent. Like the Canadian triathlete, he had a movie-star jaw and a goofy smile. He was also local, which was probably why his tweets showed up so often in Oliver's feeds...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".