The politics of third actors: strategies used by public participation professionals in their interactions with public forum sponsors
Bibliographic record
Abstract
Public participation professionals (PPPs) are individuals hired to design, implement, and facilitate participatory forums. Since PPPs are seen as third actors who ensure that dialogue between sponsors and citizens in these forums is fair and open, perceived impartiality is important to their profession. While studies on the impartiality of PPPs have mostly focused on their interactions with citizens in these kinds of forums, less attention has been paid to the role of PPPs as third actors interacting with sponsors. This article seeks to describe some strategies PPPs use with sponsors to ensure fair and open dialogue in participatory forums. Based on 35 interviews with PPPs in Quebec, six strategies employed by PPPs to maintain impartiality are described in detail. An organisational field approach was used to identify these and other strategies and capture the nature of the interdependence and mutual recognition among PPPs.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".