Enjeux de la constitution et de l’exploitation de bases de données en sociologie de l’art et de la culture
Bibliographic record
Abstract
Cet article présente certains enjeux spécifiques à la mobilisation de bases de données dans des projets de recherche en sociologie de l’art et de la culture. À partir de trois exemples concrets en histoire du livre (« Base de données des métiers du livre au Québec »), en sociologie de l’art (« Base des prix artistiques au Québec dans l’entre-deux-guerres ») et en études littéraires (« Base de données sur les figurations romanesques de la vie littéraire au xixe et xxe siècles »), il décrit les questions soulevées par ces usages particuliers de bases de données et les exploitations potentielles qu’elles permettent. Il offre aussi une réflexion générale sur la constitution et la normalisation de bases de données dans ces domaines.
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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 | 0.071 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".