Understanding Differences in Political Trust among Canada’s Major Ethno-racial Groups
Bibliographic record
Abstract
This paper considers ethno-racial differences in political trust, which leading scholars see as one of the two key dimensions of social cohesion in Canada. I compare trust among eight ethno-racial groupings: British, French, “Canadians,” other Europeans, Aboriginal Peoples, visible minorities, mixed-origins respondents, and all others. Building from the concepts of “social distance” and “social boundaries,” I test three sets of factors for explaining ethno-racial differences in trust: (1) three ethno-cultural “markers” – religion, language, and immigration status; (2) two socioeconomic influences –education and income; and (3) two social engagement indicators – voluntary association activity and ethnic diversity of friendships. Models also include controls for region, age, and gender. Using data from the 2008 General Social Survey, I find that, compared to more established groups like the British, two of the three most culturally distinctive minorities – visible minorities and French respondents – express higher political trust. Nevertheless, the third key minority community in the analysis - Aboriginal Peoples - exhibit lower political trust than all of the other groups. The findings suggests that some minorities, when treated or perceived by others as different or distant from the “mainstream,” may see government agencies as defending their minority rights and interests against discrimination. Aboriginal Peoples are a major exception to this conclusion, however. This underscores their unique position in Canada as the country’s original inhabitants, who have long endured processes of discrimination, exclusion, and racism that have influenced their trust in major government institutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".