Bibliographic record
Abstract
Bien qu’il existe plusieurs méthodes de notation pour assigner des scores aux répondants d’un questionnaire, peu d’études ont comparé les effets que pourraient avoir les méthodes choisies sur les corrélations entre les scores obtenus et d’autres variables. Cette recherche vise à combler ce manque en comparant les coefficients de corrélation entre les scores générés par sept méthodes de notation à partir de données réelles et, à défaut de données réelles accessibles, huit variables générées aléatoirement. Les résultats montrent que les corrélations sont presque identiques et qu’aucune méthode de notation n’a d’effet systématique sur la force des corrélations obtenues. Ce résultat est conforme aux résultats antérieurs et il est recommandé aux chercheurs de privilégier l’utilisation d’une méthode de notation simple et pouvant être utilisée avec des données manquantes.
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 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 | 0.131 | 0.292 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".