Une note sur le coefficient oméga (ω) et ses déclinaisons pour estimer la fidélité des scores
Bibliographic record
Abstract
Au cours des dernières décennies, certains auteurs ont suggéré de rejeter le coefficient alpha (α) de Cronbach (1951) pour adopter le coefficient oméga (ω) de McDonald (1985, 1999) basé sur un modèle d’analyse factorielle. Après avoir présenté certaines limites inhérentes à l’α, nous présentons le coefficient ω et ses déclinaisons en poursuivant deux objectifs : comprendre la logique théorique derrière les coefficients de fidélité oméga et exposer les spécificités des coefficients ω sur le plan de leur méthode de calcul. À cet effet, nous distinguons les conditions d’usage des différentes formes d’ω (total ou hiérarchique, dans le cadre d’une AFE ou d’une AFC). Un exemple d’analyse et des recommandations sont proposés pour mieux argumenter la fidélité des scores.
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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 | 0.058 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".