Quel enseignement de la finance entrepreneuriale ?
Bibliographic record
Abstract
Les besoins et modalités de financement des firmes innovantes ou à forte croissance ont donné naissance ces dernières années à un champ théorique et professionnel nouveau : la finance entrepreneuriale. Cet article propose, d’une part, de relever les particularités de ce champ relativement aux autres champs de la finance et, d’autre part, de présenter l’ancrage théorique et pratique et les enjeux pédagogiques permettant de bâtir une formation en finance entrepreneuriale. La concrétisation de ces réflexions a conduit à l’élaboration d’un programme mastère spécialisé qui illustre, en dernière partie, le propos. Nous espérons que cet article contribuera au développement des formations en finance entrepreneuriale qui, par les exigences économiques actuelles, devraient être amenées à se répandre tant en France qu’à l’international.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".