Assessing the Impacts of Public Participation: Concepts, Evidence and Policy Implications
Bibliographic record
Abstract
The expansion of ordinary citizens’ roles in a variety of policy and decision-making processes has created a pressing need to draw out the lessons from accumulated work in the field of public engagement to inform the design and evaluation of new public engagement processes. In particular, the effects of these roles on decision processes and outcomes, and on the citizens themselves, warrant scrutiny. These questions are increasingly relevant to health policy makers and health system managers working in local, provincial and national or pan-Canadian settings to find meaningful and effective ways to involve citizens in their decision-making processes. In this paper, we explore what is known about the extent to which the goals of public participation in policy have been met. The current state of knowledge about the impact of public participation on policy and civic literacy is reviewed along with the conceptual and methodological approaches to evaluation and their associated challenges. The published (English and French) empirical public participation evaluation literature is also reviewed and reflections from key informant interviews with policy makers and public participation practitioners are shared. The limits to evaluation and its uptake are discussed and strategies for advancing the practice and methods of public participation evaluation are outlined.
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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 | 0.222 | 0.449 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".