Strongholds and Battlegrounds: Measuring Party Support Stability in Canada
Bibliographic record
Abstract
Abstract.Despite the nationalization of politics in established democracies, the study of local dynamics remains fundamental to our understanding of electoral politics, especially in plurality contests. In plurality systems, local competition indeed has important consequences for the distribution of seats in parliaments and cabinets. While a plethora of measures exists to assess electoral competitiveness, none adequately captures the dynamic nature of party support at the local level. Making use of the Canadian case as an illustration, we propose a new classification of electoral districts that aims at filling this gap. Districts are divided into two categories, strongholds and battlegrounds, depending on the successive performances of parties. We argue that such a classification should be utilized in addition to static measures of competitiveness, notably in the study of political participation. Résumé.Malgré la nationalisation de la politique au sein des démocraties établies, l'étude des dynamiques locales demeure fondamentale pour bien comprendre la politique électorale, particulièrement dans les systèmes pluralitaires où la compétition locale a des conséquences importantes sur la distribution des sièges au parlement et au cabinet. Bien qu'il existe une panoplie de mesures de compétition électorale, aucune ne capture adéquatement la nature dynamique de l'appui aux partis à l'échelle locale. À l'aide du cas canadien, nous proposons une nouvelle classification des districts électoraux qui corrige cette lacune. Les districts sont divisés en deux catégories – bastions et champs de bataille – en fonction des performances successives des partis en présence. Nous suggérons que cette classification soit utilisée en plus d'autres mesures statiques, notamment dans l'étude de la participation électorale.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".