Bibliographic record
Abstract
Like many of you, I consider John Sexton to be one of the greatest teachers I've ever had. The irony, of course, is that he taught me in a classroom only three times, when I was a first-year student in Arthur Miller's Civil Procedure Class at Harvard Law School more than a quarter century ago. Then, as now, Arthur Miller was one of the most spellbinding teachers in the legal academy, so it was with bemusement that his devoted first-years watched this bearded Paul Bunyan of a man come to substitute teach. Someone whispered that he had been a divinity student. Another claimed he had turned the pigtailed St. Brendan's girls into the high school debate champs of New York. Yet another said John had dueled with Larry Tribe in the National Collegiate Debate Championships. As it turned out, all of them were right. But within seconds, we forgot John's past as he captured us in his spell, a spell that captivates me still. John spent three classes making the bland subject of joinder unforgettable, while sucking down gallons of coffee, and telling an endless stream of stories in that unmistakable Brooklyn accent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.043 | 0.014 |
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 source (direct Gemma or distilled Codex), 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".