La fidélité des scores totaux et la fidélité des scores logits : le cas du modèle de Rasch
Bibliographic record
Abstract
Nous sommes nombreux à être confondus avec les perspectives de la fidélité lors d’une analyse à l’aide du modèle de Rasch pour réponses dichotomiques. Ce court article vise à illustrer la différence existante entre la fidélité des scores totaux et la fidélité des scores exprimés sous la forme d’un logit. Pour ce faire, nous baserons notre argumentation sur deux stratégies qui quantifient la fidélité d'un ensemble de données dichotomiques unidimensionnelles dans le contexte du modèle de Rasch : la stratégie développée par Dimitrov et l'indice de séparation des personnes. Contrairement à ce que certains prétendent, les deux approches ne sont pas interchangeables. Nous allons aussi faire quelques constats pour la recherche.
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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 | 0.129 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".