Activating collective co-production of public services: influencing citizens to participate in complex governance mechanisms in the UK
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
Previous research has suggested that citizen co-production of public services is more likely when the actions involved are easy and can be carried out individually rather than in groups. This article explores whether this holds in local areas of England and Wales. It asks which people are most likely to engage in individual and collective co-production and how people can be influenced to extend their co-production efforts by participating in more collective activities. Data were collected in five areas, using citizen panels organized by local authorities. The findings demonstrate that individual and collective co-production have rather different characteristics and correlates and highlight the importance of distinguishing between them for policy purposes. In particular, collective co-production is likely to be high in relation to any given issue when citizens have a strong sense that people can make a difference (‘political self-efficacy’). ‘Nudges’ to encourage increased co-production had only a weak effect. Points for practitioners Much of the potential pay-off from co-production is likely to arise from group-based activities, so activating citizens to move from individual to collective co-production may be an important issue for policy. This article shows that there is major scope for activating more collective co-production, since the level of collective co-production in which people engage is not strongly predicted by their background and can be influenced by public policy variables. ‘Nudges’ may help to encourage more collective co-production but they may need to be quite strong to succeed.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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