Nommer, élire, tirer au sort, vendre au plus offrant? A propos du choix des juges
Bibliographic record
Abstract
Certains sujets de discussion sont pérennes et ce, que la conversation soit entre profanes ou entre experts : on ne se lassera jamais de parler de météo, du Canadien de Montréal, de la Constitution canadienne. D’autres sujets, au contraire, ne sont que récurrents, leur actualité dépendant plutôt de leurs enjeux politiques, de l’intérêt circonstanciel qu’ils engendrent, ou encore d’un vide médiatique à remplir. Pourtant, que ces sujets soient éternels ou simplement récurrents, il est rare (surtout en milieu académique) que le fait de revoir un même problème en apporte une meilleure compréhension. Pis encore, si jamais un consensus quelconque émerge ou est sur le point d’émerger, ceux et celles dont la carrière est de commenter ces sujets – qu’ils soient des chercheurs, des professeurs,des prophètes ou des politiciens – trouveront toujours une excuse pour ressemer la discorde.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".