Introduction to the special issue: facilitating citizen engagement in interactive governance
Bibliographic record
Abstract
This is an introduction to the special issue on facilitating citizen engagement in interactive governance. In this special issue, we focus on facilitators: the professionals that support and organise various forms of collaboration between governments and citizens that elsewhere have been conceptualised as interactive governance. We explore the main pressures that facilitators face and how these are negotiated in deliberative forms of policy-making, the co-production of public services, and in community-induced civic initiatives. With this exploration, we contribute to a burgeoning literature on facilitators as in-between actors, we make a unique comparison across three forms of interactive governance that are often analysed separately, we bring together disparate literatures on democratic innovation and public policy implementation, and we offer nuances and perspectives from various countries with six articles addressing facilitation in Scotland, Canada, France, the United Kingdom, Denmark and the Netherlands.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.091 | 0.033 |
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".