Le capital psychologique permet-il d’apprendre et de rebondir face à un échec entrepreneurial ?
Bibliographic record
Abstract
Bien que l’échec entrepreneurial soit perçu négativement dans la société, plusieurs chercheurs estiment qu’il offre une réelle opportunité d’apprentissage. Cependant, il est parfois difficile d’apprendre à partir d’un échec compte tenu des différents coûts financiers, psychologiques et sociaux qu’il occasionne. Au travers d’un modèle conceptuel, nous proposons un angle d’approche plus positif de l’échec entrepreneurial. Nous suggérons qu’un niveau de capital psychologique élevé joue un rôle modérateur dans la relation entre les conséquences négatives de l’échec et l’apprentissage à partir de l’échec. Cet apprentissage et ce capital psychologique élevé aideraient l’entrepreneur ayant échoué à poursuivre sa carrière entrepreneuriale.
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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 | 0.002 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".