Do Polarization Narratives Apply to Politics on the Periphery? The Case of Atlantic Canada
Bibliographic record
Abstract
Researchers, policymakers, and the public often claim that "extreme" political views have become increasingly commonplace and that polarization on issues of race and immigration has become a central dilemma for contemporary politics. The popular narrative of political polarization captures tensions that many are noticing and experiencing. However, there is also significant confusion around the concept, as well as gaps between popular perceptions and empirical findings on the different forms of polarization and their prevalence across regions. It is unclear to what extent polarization describes a global phenomenon, as its national and subnational manifestations vary considerably, produced from distinct local histories as well as diffuse transnational forces. While the United States is often treated as ground zero for political polarization, nearby Canada does not appear to be experiencing polarization to nearly the same degree. Using data from a 2019 survey on Atlantic Canadians' political views and perceptions of change, this paper examines whether underlying forms of political polarization are manifesting in the region. We assess whether mass ideological polarization and partisan sorting can be found in Atlantic Canada, looking at socio-cultural and economic dimensions of political values. We also examine perceptions of polarization in the region, using Multiple Correspondence Analysis to observe underlying associations between perceptions, extreme or polarized views, and partisanship. This mapping approach provides insight into latent patterns often missed by more traditional methods.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".