Taxonomie des profils de motivation chez les étudiants déclarant une aspiration à l’identité entrepreneuriale
Bibliographic record
Abstract
Notre recherche ambitionne d’identifier, à l’aide d’une classification inductive, différents profils de motivations chez les étudiants ayant une aspiration entrepreneuriale. L’analyse de onze facteurs de motivations entrepreneuriales au sein d’un échantillon de 256 répondants a tout d’abord permis de faire émerger une taxonomie spécifique composée de six profils distincts. La réalisation de plusieurs analyses en covariance a ensuite permis de démontrer que le degré d’intention entrepreneuriale varie significativement selon le profil mais seulement lorsque les résultats sont contrôlés par certaines variables individuelles (le sexe, l’âge, le niveau d’étude, la formation en entrepreneuriat et l’expérience entrepreneuriale personnelle).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".