Bibliographic record
Abstract
Despite its racist history, multiculturalism policy helps Canada garner a significant international reputation for promoting cultural diversity. It seems that racism suddenly disappears and Canada becomes a multicultural country. Then, how should we understand Canada&s;s multiculturalism? This chapter traces the changing domestic and international context since the Second World War and its implication for Canada&s;s immigration policy and the emergence of multiculturalism. By critically examining its origin and evolvement, it argues that Canada&s;s multiculturalism policy should be understood as a product of political struggles, a strategic tool to maintain the domination of English Canada and to assimilate racialized minorities. This chapter discusses two major academic debates over the past decades, including multiculturalism as a politics of recognition and multiculturalism as a divisive force. It suggests that unequal power relations between the dominant groups and racialized minorities do not change despite the rhetoric of multiculturalism. Further, although multiculturalism claims to promote cultural diversity, by separating language and culture rights as institutionalized in multiculturalism within a bilingual framework, it is really difficult for ethnic groups to maintain their ethnic language and cultural heritage. The interview data from my study with Chinese Canadian youth further support this point.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".