Issue Accountability in Non-Partisan Municipalities: A Case Study
Bibliographic record
Abstract
Issue accountability – the extent to which elected representatives are rewarded or punished by voters for their legislative actions in office – is fundamental to many conceptions of healthy democratic accountability. It is not clear, however, if this form of accountability is possible in non-partisan contexts, when constituents may have considerably more difficulty acquiring information about what their elected representatives have done. In this research note, we use data from council roll calls and a large public opinion survey to provide a case study of issue accountability in a large non-partisan city in Canada, assessing how citizens’ agreement with their elected representatives’ actions on seven high-profile policy issues is related to their satisfaction with their representatives’ performance. We find that most local residents are unaware of or incorrect about their councillors’ actions in office, even on issues that they consider important. However, we also find that issue alignment is very strongly related to performance satisfaction among citizens who do know how their councillors have acted in office. Our findings thus illustrate both the possibility of issue accountability in non-partisan municipal politics as well as its constraints.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".