Bibliographic record
Abstract
their suggestions on an earlier version of this paper. The authors received helpful comments from seminar participants at the Université Laval, the World Bank, the Université de Paris-I (Panthéon-Sorbonne) and the Université d’Orléans. The financial support of the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. The usual disclaimer applies. RÉSUMÉ Les études antérieures sur les déterminants du choix d’une filière universitaire ont présumé une probabilité constante de succès entre les différentes filières d’études ou des revenus constants entre les filières. Notre modèle dépasse ces deux hypothèses restrictives en construisant une variable de revenus anticipés pour expliquer la probabilité qu’un étudiant choisisse une filière parmi quatre domaines de spécialisation. La construction d’une variable de revenus anticipés exige de l’information sur la probabilité de succès perçue par l’étudiant, sur les revenus estimés des diplômés dans toutes les spécialisations et sur les revenus alternatifs de l’étudiant s’il échoue à l’obtention de son diplôme. En utilisant des données du National Longitudinal Survey of Youth, nous
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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".