The Power of the Dark Side: Negative Partisanship and Political Behaviour in Canada
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 The origins and implications of partisan identification are well-studied, but negative partisan attitudes—dislike for a particular party—have escaped such scrutiny, even as the politics of negativity enjoys sustained popularity, especially come election time. In this paper we build upon the comparatively modest negative partisanship literature to consider the effects of negative partisan attitudes on a range of political behaviours. There are reasons to suspect that negative and positive partisanship may have different effects; thus, accounting for the unique influence of negative attitudes is important for understanding the full effect of partisanship on political behaviour. Our results, based upon Canadian Election Study data from 2008 and 2011, reveal that, in addition to vote choice, negative partisanship influences voter turnout and a range of political activities, both related and unrelated to parties. These findings provide evidence of the power of the “dark side” of partisanship.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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