Bibliographic record
Abstract
La culture choisit la pauvrete ou la prosperite; l'humain choisit la culture: l'humain choisit la pauvrete ou la prosperite. C'etait vrai hier, ce l'est aujourd'hui, et ce le sera demain. Dans ce constat, on peut voir que la notion de choix est determinante dans le destin des nations. Ce choix, c'est bien celui de leurs politiques. Pres de 90 % des pays francophones vivent encore sous perfusion de l'aide au developpement, et ce, dans une des rares formes de pauvrete. Ce livre, base sur l'evaluation du Rendez-vous entrepreneurial de la Francophonie, une etude empirique aupres de 10 pays francophones, pose un diagnostic sans complaisance des politiques deployees par ces ces derniers en matiere de promotion de la culture entrepreneuriale et de l'entrepreneurship. Ce sont: la Belgique, le Burkina Faso, le Canada, la Cote d'Ivoire, la France, Madagascar, le Senegal, l'Ile Maurice, la Tunisie et le Vietnam. Dans ces pays, nous nous sommes interesses strictement aux leaders politiques, socio-economiques ainsi que ceux des medias et de l'education. L'ouvrage leve le voile sur la specificite des politiques des pays francophones et les mythes auto-asphyxiants entretenus en la matiere.
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 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 | 0.005 | 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 | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".