Intelligence collective et résilience entrepreneuriale à l’ère de la Covid-19
Bibliographic record
Abstract
Notre article améliore la compréhension du pouvoir explicatif de l’intelligence collective sur la résilience entrepreneuriale, grâce à l’ancrage du management collaboratif. Notre objectif consiste à proposer un modèle de résilience entrepreneuriale en contexte de Covid-19 afin de contribuer efficacement à la relance économique. La collecte des données a été réalisée au moyen de 15 entrevues et 282 enquêtes par sondage auprès des entrepreneurs résilients camerounais et tchadiens. Nos principaux résultats montrent que l’intelligence collective, via ses principales composantes, est une posture et un moyen privilégié qui accroît la résilience entrepreneuriale économique et stratégique à l’ère incertaine, ceci s’explique par le fait que les entrepreneurs qui survivent durant la Covid-19 font recours aux réseaux d’affaires, aux capacités dynamiques et aux élans de solidarité. De ce fait, les entrepreneurs procéderaient à l’instrumentation des constituantes de l’intelligence collective afin d’assurer leur résilience.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".