Write the Rules and Win: Understanding Citizen Participation Game Dynamics
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
In attempting to move beyond normative‐based theories and simple descriptive accounts of extra‐electoral citizen participation, this article explores the biases that are inherent in citizen participation mechanisms and proposes a model to estimate when and why different participation mechanisms might be used during “citizen participation games.” Mechanism bias is explored using a matrix designed to gauge how different mechanisms afford different degrees of agenda‐setting and decision‐making control to citizens and state officials. Attention then turns to leadership capacity to explain the mechanisms through which teams of citizens and government officials might play their participatory games. A second matrix suggests that the choice of mechanism may vary considerably depending on whether rookie leaders are matched against other rookies, novices, or expert opponents. Though the model suggests that mechanisms affording less control to citizens are more common, it also implies that in the future, citizen players may demand mechanisms affording them more control as their leaders gain experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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