Activating collective co-production of public services: influencing citizens to participate in complex governance mechanisms in the UK
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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How this classification was reachedexpand
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".