Crowdsourcing d’activités inventives et frontières des organisations1
Bibliographic record
Abstract
Le crowdsourcing d’activités inventives (CAI) consiste à externaliser à une foule des activités de recherche, des tâches complexes ou créatives. Plusieurs exemples récents ont mis en avant les avantages de ce type de pratique, aussi bien pour réduire les coûts du processus d’innovation que pour améliorer ses résultats. Dans ce travail, nous étudions l’impact organisationnel du CAI en mobilisant les théories sur les frontières des organisations. La littérature identifie quatre frontières différentes : frontière d’efficience, d’influence, de compétence et d’identité. L’analyse en termes de frontière des organisations nous permet notamment de mettre en avant certaines limites du recours au CAI et d’élaborer des prédictions théoriques sur les conditions d’émergence et d’utilisation du CAI.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".