Polarization Eh? Ideological Divergence and Partisan Sorting in the Canadian Mass Public
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract There has been increasing concern among commentators and scholars about polarization in Canada. This note uses the Canadian Election Study from 1993 to 2019 to measure trends in ideological divergence, ideological consistency, and partisan-ideological sorting in the Canadian mass public. It finds only mixed evidence that Canadians are diverging ideologically and becoming more polarized—ideological distributions are unimodal and trends toward more dispersion are slight and driven entirely by the last two election cycles. Canadians are, however, becoming modestly more ideologically consistent and much more sorted—that is, partisanship, ideological identification, and policy beliefs are increasingly interconnected. These findings call for additional research on the causes and consequences of mass polarization in Canada and further efforts to situate these results, along with findings from the United States, in a comparative context.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it