Décryptage des demandes de financement des entrepreneures : une étude de l’appropriation de leur héritage familial extrafinancier
Bibliographic record
Abstract
Les demandes de financement des femmes entrepreneures restent encore peu explorées par la littérature scientifique. Pour investiguer ce sujet, cette recherche vise à comprendre comment les dynamiques familiales influencent les choix financiers des entrepreneures. En s’appuyant sur la théorie des scripts familiaux, elle analyse comment l’héritage familial, dans sa dimension extrafinancière symbolique, influence les demandes de financement des entrepreneures. Les entretiens narratifs menés auprès de 41 entrepreneures montrent en quoi la famille est le lieu d’héritages liés à des éléments tels que la condition financière familiale, la gestion des finances, les discours sur l’argent et les influences culturelles et sexistes, qui façonnent l’approche des entrepreneures envers le financement. Trois types de scripts familiaux sont identifiés : réplicatifs, correctifs et intégratifs, qui conduisent les entrepreneures soit vers la recherche de financements externes, soit vers l’autofinancement. En abordant ces aspects peu explorés, cette recherche enrichit la compréhension des enjeux du financement de l’entrepreneuriat féminin.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".