Refonte de l’inventaire typologique des intérêts professionnels (ITCP-72-R) : premières évidences de validité basées sur la structure interne de sa nouvelle adaptation au contexte franco-québécois
Bibliographic record
Abstract
Tout d'abord, j'aimerais tout particulièrement remercier chaleureusement mes parents, Francine Mercier et Gilles Alarie, qui m'ont énormément supporté durant l'ensemble de mes années d'études et qui m'ont permis d'accomplir sereinement ma scolarité en psychologie.Je suis pleinement conscient des sacrifices que vous avez faits et j'en serai éternellement reconnaissant.Par ailleurs, j'aimerais grandement remercier mon directeur de recherche, Marcos Balbinotti, qui m'a partagé ses connaissances à l'égard du domaine de la psychométrie et accompagné durant l'accomplissement de ce projet de recherche qui culmine avec la rédaction de cette thèse.Finalement, j'aimerais également remercier Cynthia Mathieu et Annick Parent-Lamarche, membres de mon comité doctoral, pour m'avoir partagé leurs connaissances en psychologie organisationnelle et notamment, au niveau des différents traits de personnalité et de leurs adéquations avec les divers milieux de travail.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".