Conceptualization, measurement and effects of positional issues in the Canadian electoral context
Bibliographic record
Abstract
It is a well-known fact that the major Canadian political parties now use political marketing tools to segment the electorate and target specific groups of voters. Positional issues are at the centre of this type of micro-targeting strategy. This article demonstrates that positional issues played a greater role in Canadian electoral politics than previously assumed. Despite the many theoretical reasons for why the effects of positional issues might have been overlooked, accounting for disaggregation error shows stable and consistent effect of positional issues on vote choice in Canada. Multi-item issue scales are used to test the stability and relative strength of positional issues compared to rival concepts, such as values and party identification. Once measured through aggregated items, the effects of positional issues on vote choice in Canada might even compare with those of conventional vote predictors, such as party identification. Hence, it shows that the actions parties take to capitalize on positional issues, as described by the political marketing literature, are justified.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".