Lettre ouverte sur l'importance du féminisme dans l'écriture scientifique
Bibliographic record
Abstract
Les données des participantes aux études en psychologie sont systématiquement exclues des études publiées. Du moins, c’est ce que certaines naïves gens pourraient penser lorsqu’elles lisent des articles, affiches ou résumés d’études en psychologie. Effectivement, il n’est pas rare de lire dans les articles ou autres écrits scientifiques les lignes suivantes : « les participants prennent part à une tâche… » ou « les participants remplissent le questionnaire… » ou même « les résultats des participants suggèrent que… ». Pourtant, les sujets qui participent aux expériences dans les laboratoires de l’École de psychologie sont bien souvent des participantes, des femmes; ces dernières sont d’ailleurs majoritaires au sein de notre département ! Pourquoi ne sont-elles donc pas identifiées comme telles ?
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 | 0.146 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".