Personality and (Negative) Partisanship in Canadian Federal Politics
Bibliographic record
Abstract
This piece provides an in-depth examination of the relationship between personality and affective orientations (both positive and negative) towards political parties in a multi-party system. Using data from an original survey of nearly 1500 Canadians, it considers the questions of how personality traits are related to positive and negative partisanship, as well as how these traits drive partisanship towards the four major parties in English Canada’s national party system. It uses more comprehensive measures of personality than does similar previous work – specifically, it employs the HEXACO model of personality, measured through a 60-item battery. Data reveal that personality is an important driver or both positive and negative partisanship, that it effect the two types of partisanship differently and that different traits are associated with support for, or opposition to, each of Canada’s major political parties. These findings demonstrate the importance of personality for understanding partisanship, but these relationships are complex and party-specific in a multi-party setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".