Policy Priorities of Municipal Candidates in the 2014 Local Ontario Elections
Bibliographic record
Abstract
This paper reports the results of a survey on the policy priorities of municipal candidates in the 2014 municipal elections in Ontario. As part of a survey of municipal candidates in 47 Ontario municipalities, we asked a series of questions relating to perceived policy priorities, election issues, and electoral success to shed light on the extent to which municipal political candidates are “policy seekers,” and the extent to which their policy priorities vary across municipalities and municipal types, successful and unsuccessful candidates, and urban and rural candidates. We find that reported policy priorities tend to fall into two major categories: fiscal issues and economic development or administration and good governance. The prominence of these fiscal and procedural priorities is steady across a range of local candidate types, including successful and unsuccessful candidates, incumbent and non-incumbent candidates, and even urban and rural candidates. Only in very large municipalities, according to our findings, does the structure of candidate priorities begin to diverge from this standard emphasis on finance and procedure.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".