Comment faire de la pseudoscience avec des données réelles : une critique des arguments statistiques de John Hattie dans Visible Learning par un statisticien
Bibliographic record
Abstract
Cet article offre une critique du point de vue d’un statisticien de la méthodologie utilisée par Hattie, et explique pourquoi il faut absolument qualifier cette méthodologie de pseudoscience. On parle tout d’abord des intentions de Hattie. Puis, on décrit les erreurs majeures de Visible Learning avant d’expliquer l’ensemble des questions qu’un chercheur devrait se poser en examinant des études et enquêtes basées sur des analyses de données, incluant les méta-analyses. Ensuite, on donne des exemples concrets démontrant que le d de Cohen (la mesure de base derrière les effets d’ampleur, effect sizes , de Hattie) ne peut tout simplement pas être utilisé comme une mesure universelle d’impact. Enfin, on donne des pistes de solution pour mieux comprendre et exécuter des études et méta-analyses en éducation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.496 | 0.776 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.012 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".