Aide à la décision multicritère : cohérence et équité grâce à l’analyse de concepts
Bibliographic record
Abstract
De nombreuses décisions sont prises en commission, par exemple pour affecter des ressources. Les critères de décision sont difficiles à exprimer et la situation globale est en général trop complexe pour que les participants puissent l’appréhender pleinement. Dans cet article, nous décrivons un processus de décision pour la sélection de candidats à un emploi. L’analyse de concepts y est utilisée pour faire face aux problèmes mentionnés ci-dessus. Grâce à l’analyse formelle de concepts et aux systèmes d’information logiques, les personnes fair play ont la possibilité d’être équitables envers les candidats et de faire preuve de cohérence dans leurs jugements sur toute la durée du processus de décision.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".