Living Labs et territorialisation de politiques publiques innovantes. L’exemple du Living Lab Jeunesse de la Métropole de Lille (France)
Bibliographic record
Abstract
Les Living Labs peuvent être initiés par la société civile pour pallier les manques de l’État-providence, mais cet outil est de plus en plus mobilisé par la puissance publique pour associer des usagers à la transformation des politiques publiques. L’article propose trois types de Living Labs et trois scénarios d’usage pour innover dans l’action publique, selon l’engagement des citoyens, des parties prenantes et des décideurs publics. Ce modèle est testé sur le cas du « Living Lab Jeunesse » de la Métropole de Lille (France), expérimentation financée sur appel à projets national pour co-construire avec les jeunes une politique sociale innovante à l’échelle d’un territoire métropolitain. Cette étude de cas montre comment les institutions publiques peuvent articuler les trois types de Living Lab sur le temps long pour transformer l’action publique étape par étape.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".