Beyond Regions and Ideology: Using Cultural Theory to Explain Risk Perception in Canada
Bibliographic record
Abstract
Abstract Cultural theory (CT) has been widely used to explain variations in risk perception but has rarely been tested in Canada. This contribution represents the most thorough attempt to adapt CT to the Canadian context. Study results suggest that respondents’ commitment to egalitarianism was strongly correlated with risks from technology, while respondents’ commitment to hierarchism was strongly correlated with risks from criminal or unsafe behaviours. Respondents’ commitment to individualism was also correlated with risks from criminal and unsafe behaviours but differed from hierarchism in that individualism was not correlated with risk perceptions from prostitution and marijuana use. Respondents’ commitments to fatalism were strongly correlated with risk perception of vaccines. These conclusions are reinforced by results from a survey question that tests the extent to which such cultural predispositions map onto the myths of nature hypothesized by CT and by a survey experiment that tests how cultural commitments predict perceived risks from a controversial pipeline.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".