Contribuer à l'émergence d'une intelligence collective entrepreneuriale dans un projet collaboratif interorganisationnel
Bibliographic record
Abstract
Afin d’obtenir des occasions d’affaires et d’assurer leur compétitivité ou leur pérennité, certaines petites entreprises s’insèrent dans des stratégies collaboratives. Elles s’associent entre elles autour d’un projet qu’elles n’auraient pas pu, su ou voulu mener seules. Cependant, faire émerger une intelligence collective n’est pas un processus naturel en soi, et encore moins dans une configuration interorganisationnelle. Cet article vise à dégager les déterminants théoriques de l’émergence d’une intelligence collective entre des petites entreprises. Par la suite, nous confrontons ces déterminants théoriques avec l’abandon précoce d’un projet innovant collaboratif. Enfin, nous formulons des recommandations de gestion en vue de favoriser l’émergence d’une intelligence collective entre de petites entreprises.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".