Les stratégies de crowdsourcing pour innover : quels enjeux ? Le cas des banques françaises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
L’objectif de cet article est d’explorer les enjeux du recours au crowdsourcing pour innover dans le secteur bancaire. Fondée sur une méthodologie qualitative, notre recherche souligne qu’au travers de cette modalité d’externalisation, les banques voient l’opportunité d’ouvrir le dialogue avec la foule des internautes et de bénéficier, dans un processus de création de valeur, de leurs idées. Alors que les bénéfices du recours au crowdsourcing semblent prégnants, notre recherche témoigne également de sources de déception et de vigilance. En particulier, pour des raisons juridiques ou stratégiques, les banques se heurtent à l’impossibilité de mettre en place certaines idées des internautes.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it