Coproduction and the third sector in France: Governmental traditions and the French conceptualization of participation
Bibliographic record
Abstract
Abstract Research on coproduction has tended to assume a coherence of conceptualizations of coproduction across borders, and little analysis of the framing and discourse of coproduction in different contexts has been undertaken. In the French language literature on citizen participation and the social and solidarity economy, the term coproduction is little used. This paper investigates the narratives of French academics, public, and third sector actors in order to identify what, if anything, is different about the French context that explains this gap. Drawing on semistructured interviews, I identify four key narratives that distinguish the French conceptualizations of coproduction and the third sector from the dominant English language coproduction literature: (a) a mainstreaming of coproduction as part of organizational purpose in the social and solidarity economy, (b) an emphasis on formalized involvement of citizens in organizational governance, (c) the motivation of citizen empowerment and democracy over cost and efficiency, and (d) the use of the term coconstruction rather than coproduction. I argue that these narratives are shaped by the governmental traditions of France, which emphasize formal rules, hierarchy, representative democracy, and a suspicion of particularistic interests. I conclude by questioning the universality of some of the axioms of coproduction theory in the English language literature.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".