Évolution des critères d’investissement des business angels : de la présélection des projets à l’investissement final1
Bibliographic record
Abstract
Cette étude sur les réseaux structurés de business angels porte sur l’évolution des critères de sélection des projets entrepreneuriaux en vue d’un financement, entre deux stades successifs du processus, à partir de données réelles et d’évaluations à chaud. Au niveau de la présélection, les instructeurs chargés d’étudier les projets exigent de tout projet d’être porté par des entrepreneurs qui présentent des compétences managériales fortes tout en proposant simultanément un plan d’affaires qui paraît crédible. Au stade de la plénière en revanche, les business angels se soucient particulièrement de l’avantage concurrentiel des projets. Les entrepreneurs doivent inspirer confiance aux business angels tout au long du processus de sélection alors que leur capital social ne semble pas pris en compte par ceux-ci.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".