Business models de l’entreprise et ONG : contributions du portefeuille de partenariats
Bibliographic record
Abstract
Les grandes entreprises sont soumises à des enjeux sociétaux qui menacent leur développement. Pour assurer leur pérennité, certaines décident de développer des partenariats diversifiés avec des organisations non-gouvernementales (ONG). A partir d’une étude de cas approfondie, nous identifions différents types de contributions des partenariats ONG-entreprise à la stratégie d’une multinationale. Au niveau corporatif, ils participent au pilotage des activités stratégiques de l’entreprise, et au niveau business, ils contribuent de façon complémentaire au déploiement des différents modèles d’affaires. Ces résultats permettent de repenser la contribution des ONG à la stratégie de l’entreprise à travers le concept de portefeuille de partenariats ONG-entreprise.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".