Élaborer un test d’intérêts pour l’auto-orientation : problèmes de conception, de validation et d’utilisation
Bibliographic record
Abstract
Pour qu’un sujet puisse s’auto-orienter, il doit pouvoir disposer d’un instrument d’évaluation directement interprétable et qui soit de valeur métrologique assurée. Ces deux thèses sont illustrées par l’exemple du questionnaire QIPOS. La première partie de l’article présente sa construction et son utilisation souhaitable. La seconde montre la façon dont il faudrait contrôler sa validité et sa fidélité, dans cette situation de décision particulière, où les comparaisons sont à faire entre motivations plutôt qu’entre personnes. Un exemple minimal est présenté, traitant de deux groupes en deux dimensions, pour montrer le principe de l’étude de généralisabilité qui serait à réaliser.
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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 | 0.093 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".