Bibliographic record
Abstract
Canada is on the threshold of a major transformation in its ethnic and racial profile. Relative rates of immigration from European and non-European countries have reversed during the past 40 years. What impact has this had on the attitudes of the dominant white majority? This article provides a partial answer to this question by examining the attitudes and voting behaviour of this majority when faced with increasing concentrations of visible-minority neighbours. Attitudinal data from the 1997 and 2000 Canadian Election Studies were combined with data from the 1996 census to measure socio-economic status and the visible-minority population of the neighbourhoods occupied by survey respondents. Regression analysis reveals two different contextual effects involving the attitudes of the majority toward visible minorities. Positive attitudes towards visible minorities increase with neighbourhood socio-economic status, a finding consistent with "social identity" theory. However, positive attitudes toward racial minorities decline when the visible-minority population increases, a finding consistent with "realistic conflict" theory. Both contextual effects have implications for voting. Independent of individual characteristics, white voters are more likely to support Reform/Alliance if they live in less-educated environments or among higher numbers of visible-minority neighbours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".