Bibliographic record
Abstract
Abstract In the past twenty years, there has been a growing pessimism on the effects of ethnic diversity. Studies are dominated by the assumption that ethnic diversity is a problem along multiple dimensions. Studies also suggest that countries marked with high levels of ethnic diversity are less peaceful, less democratic, underdeveloped, and negligent to the needs of the poor. In short, ethnic diversity is seen as a dysfunction for modern societies, and is moreover viewed as a threat to political systems, as ethnic minorities are now seeking public recognition in the form of multiculturalism and minority rights. This article discusses ethnic diversity in Canada. Against the background of the pessimistic view on diversity, Canada is an exception. It contains high levels of ethnic, linguistic, and religious diversity. Moreover, although Canada supports multiculturalism and minority rights, it remains peaceful and enjoys a prosperous democracy with a reasonably well-developed welfare state. The Canadian experience suggests that the effects of ethnic diversity and identity politics are not predetermined, and that a multicultural form of citizenship is possible. In the following discussions of the article, the basic features of the Canadian approach to ethnic diversity, including the controversies and challenges of Canadian diversity, are examined.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".