Évaluation de l’utilisation et de la présentation des résultats d’analyses factorielles et d’analyses en composantes principales en éducation
Bibliographic record
Abstract
Nous évaluons l’utilisation et la présentation des résultats d’analyses factorielles et d’analyses en composantes principales dans six périodiques canadiens en éducation publiés entre 1995 et 2005. À partir de 1089 articles, nous avons relevé 61 utilisations de ces analyses. En ayant recours à une grille de lecture, nous avons recueilli des informations sur le but de l’utilisation de l’analyse, la taille de l’échantillon, les variables, la méthode d’extraction, le critère de dimensionnalité, la méthode de rotation des axes et les résultats présentés. Nous avons identifié des lacunes, tant au niveau des pratiques que de la présentation des résultats. Enfin, nous formulons quelques recommandations quant à l’utilisation rigoureuse de ces types d’analyses.
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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 | 0.151 | 0.377 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".