Les impacts d’une évaluation bibliométrique standardisée : le cas des publications de sciences sociales au sein d’une section universitaire multidisciplinaire
Bibliographic record
Abstract
En France, le Hcéres place depuis 2018 la bibliométrie d’impact au coeur du processus d’évaluation des unités de recherche en STAPS. L’objectif de cet article est d’analyser les effets de ce virage en matière de valorisation des productions en sciences sociales, lors des évaluations de laboratoire comme sur les pratiques scientifiques des principaux intéressés. Les rapports d’évaluation de six laboratoires et douze entretiens réalisés auprès d’enseignants-chercheurs montrent que le nouveau guide des produits de la recherche altère en profondeur leur activité. Il est appliqué avec un zèle qui renforce la centralité duScimago Journal Ranking, devenu outil normatif d’appréciation. Les enseignants-chercheurs concernés élaborent des stratégies duales pour composer avec ces nouvelles exigences tout en se ménageant des espaces d’expression et de reconnaissance alternatifs.
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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 | 0.186 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".