Political Attitudes and Behaviour in a Non-Partisan Environment: Toronto 2014
Bibliographic record
Abstract
Abstract Voting behaviour in municipal elections is understudied in Canada. Existing research is limited by the type of data (aggregate instead of individual-level) and the cases evaluated (partisan when most contests are non-partisan). The objective of this study is to contribute to this literature by using individual-level data about a non-partisan election. To do so, we use data from the Toronto Election Study, conducted during the 2014 election. Our research goals are to evaluate whether a standard approach to understanding vote choice (the multi-stage explanatory model) is applicable in a non-partisan, municipal-level contest, and to determine the correlates of vote choice in the 2014 Toronto mayoral election in particular. Our analysis reveals that, although it was a formally non-partisan contest, voters tended to view the mayoral candidates in both ideological and partisan terms. We also find that a standard vote choice model provides valuable insight into voter preferences at the municipal level.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".