Provincial Battles, National Prize?
Bibliographic record
Abstract
In parliamentary systems like Canada, voters directly contribute to the election outcome only in their own riding. However, the focus of election campaigns is often national, emphasizing the leader rather than the local candidate, and national rather than regional polls. This suggests that elections are national contests, but election outcomes clearly demonstrate that support for parties varies strongly by province. Focusing on the 2015 Canadian election campaigns in British Columbia, Ontario, and Quebec, three large provinces with different subnational party systems, Provincial Battles, National Prize? evaluates whether we should understand elections in Canada as national wars or individual provincial clashes. The authors draw upon voter and candidate surveys, party campaign behaviour, and media coverage of the election to document how political parties vary their messages and strategies across provinces, how the media communicate and frame those messages, and how voters ultimately respond. The study shows that provincial variations in party support reflect differences in voters' political preferences rather than differences in party messages or media coverage. A novel and comprehensive study, Provincial Battles, National Prize? is the first and only thorough treatment of the party, media, and voter aspects of a federal election campaign through a subnational lens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".