Bibliographic record
Abstract
Le coût du financement par les fonds propres (coût du capital) est un élément essentiel de l’évaluation de l’entreprise, mais aussi des opérations de financement et de l’analyse des projets. Toutefois, estimer le coût du capital entrepreneurial est un exercice extrêmement difficile. Les modèles classiques de la finance, adaptés aux grandes entreprises, ne peuvent pas s’appliquer dans ce cadre particulier. Les données de marché, essentielles aux estimations, sont inexistantes. Nous présentons, illustrons et critiquons les principales méthodes proposées en la matière. Nous proposons par ailleurs la seule estimation récente des primes de taille applicable à des entreprises de très faible capitalisation. Ni les pratiques ni les modèles théoriques ne fournissent actuellement de solution satisfaisante au problème de l’estimation du coût du capital entrepreneurial. Nous suggérons donc plusieurs avenues de recherche visant à mieux comprendre et estimer ce coût.
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 | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".