Bibliographic record
Abstract
It was a sunny day when we all met in a classroom at McGill University The gathering went on all day and at the end someone proposed writing up the discussion as essays. Hence, this collection.\nI’d like to take a moment of gratitude to express heartfelt thanks to all the participants. And especially to Vincent Forray and Jean d’Aspremont for organizing the event, and to Genevieve Renard Painter and Liam McHugh-Russell for bringing this collection over the finish line. I don’t know whether the intellectual generosity of the participants was because of Canada, or Montreal, or McGill, or the Law School, or because of the people assembled in that room (though surely it must have been all the above to some degree). The gathering was what legal academia ought to be—people exploring ideas, thinking about possibilities, and reflecting on the professional contexts that shape their work. There was also a certain amount of play. The latter, play, is not required for academic thought, but it is essential to intellectual activity. Not frivolous play, but serious play. Without the ludic element and the freedom it implies, it’s all pretty much connect-the-dots, adjust the curves, fill in the blanks, and make sure that the disclaimers and burdens of persuasion are tight and tough.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.143 | 0.029 |
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".