Bibliographic record
Abstract
RemerciementsCe livre est tiré d'une thèse de doctorat déposée à l'Université de Montréal et à l'Université Paris IV-Sorbonne.Les recherches qui ont mené à sa soutenance, en décembre 2007, ont bénéficié de l'appui financier du Conseil de recherches en sciences humaines du Canada, du Fonds québécois de la recherche sur la société et la culture, du Programme de soutien aux cotutelles de thèse du ministère des Relations internationales du Québec ainsi que du Département des littératures de langue française de l'Université de Montréal.Son aboutissement est redevable au soutien précieux des nombreuses personnes qui ont accompagné de près ou de loin sa rédaction.Ma gratitude va d'abord à Michel Delon et à Benoît Melançon, qui furent des codirecteurs de thèse et des lecteurs aussi exigeants que généreux.La justesse et la rigueur de leurs remarques n'ont pas manqué de guider une entreprise que la matière rendait pourtant susceptible de bien des écarts.
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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.187 | 0.094 |
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".