Conditions for Efficacious Petitions: Empirical Evidence from Two Cities
Bibliographic record
Abstract
This article sheds some light on the widely exercised but little understood political activity of the petition and its potential in local politics. It posits the petition as a tool of group politics used by citizens to obtain some favorable policy decision. Among many factors that determine the success of an initiative, three are examined: (1) resources, operationalized simply as number of names collected; (2) media attention, which can raise the salience of the issue; and (3) coherence, operationalized as the number of issues a petition implicates. Hypotheses are tested against evidence gathered from 126 petitions circulated in two municipal regions. Results imply the petition can help facilitate greater citizen involvement in public affairs. Este artículo intenta esclarecer el ampliamente practicado pero poco comprendido ejercicio de las peticiones políticas y su potencial en la política a nivel local. En él se postula a la petición política como una herramienta de la política grupal usada por los ciudadanos para obtener una decisión política favorable. Entre los factores que determinan el éxito de una iniciativa, tres son examinados: (1) recursos, considerados como el número de nombres recolectados; (2) cobertura por los medios de comunicación, que puede aumentar la importancia del tema; y (3) coherencia, considerado como el número de consecuencias que implica una petición. Hipótesis son corroboradas con evidencia recolectada de 126 peticiones puestas en circulación en dos regiones municipales. Los resultados implican que las peticiones pueden ayudar a facilitar una mayor participación ciudadana en asuntos públicos.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".