Effets du niveau de scolarité sur la survie des petites et moyennes entreprises féminines en République du Congo
Bibliographic record
Abstract
Le présent article a pour visée d’analyser les effets du niveau de scolarité sur la survie des petites et moyennes entreprises féminines en République du Congo. Réalisée à partir des données du Recensement général des très petites, petites et moyennes entreprises (RTPME) de 2017 et conformément au modèle de régression logistique (ou logit), l’analyse a permis d’obtenir au moins deux résultats. Premièrement, le diplôme le plus élevé obtenu par la promotrice améliore la survie des petites et moyennes entreprises féminines en République du Congo. Deuxièmement, l’âge de celle-ci au moment de la création et son accès au financement influencent significativement la survie des PME féminines. Ces résultats conduisent à la formulation de quelques recommandations de politiques économiques.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".