Mesure d’impact d’une variable binaire sur une réponse quantitative dans un cadre non paramétrique
Bibliographic record
Abstract
On propose dans cet article une méthode de quantification de l’effet d’une variable explicative qualitative sur une réponse quantitative plus souple à utiliser que le simple coefficient d’un modèle GLM multiplicatif ; l’objectif est de disposer d’une mesure ne nécessitant pas l’hypothèse de proportionnalité du GLM et complètement décorrélée de l’effet des autres variables explicatives incluses dans le modèle. L’approche est illustrée à l’aide de données de coûts de sinistres matériels automobiles, pour lesquelles on cherche à quantifier l’impact de l’expert qui a évalué le sinistre sur le montant de l’évaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".