Understanding co-production as a new public governance tool
Bibliographic record
Abstract
Abstract Co-production has become a buzzword for both scholars and practitioners in the past decade. This introduction to the thematic issue ‘Co-production: Implementation problems, new technologies and new designs’ unpacks the concept of co-production and illustrates how it has been operationalized on the ground in diverse country-specific contexts. To facilitate the analysis, we make a distinction between ‘traditional’ and ‘non-traditional’ forms of co-production, even though the practice has not really been around long enough to have established a tradition in the true sense of the word. However, these two distinct forms of co-production are highly useful conceptual lenses through which to view the finer details and nuances, to identify the enabling conditions and to foreshadow the governance challenges, but also to highlight the innovating role co-production plays in forging public services and public policies. Thanks to the rich and varied ways in which the contributors have approached this central topic; the thematic issue enables the research and practice to more fully appreciate the ins and outs of co-production and suggests the most promising directions for future study.
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 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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".