Bibliographic record
Abstract
Multiculturalism-both in a sociological and philosophical sense-is premised on and promotes a certain plurality of values.Whether this plurality extends to multiculturalism itself remains an open and politically loaded question.The diversification of popular criticism of multicultural policies in Canada, and the growing presence of the term kyosei (coexistence) in Japanese political discourse as an alternative to "Western" multiculturalism are but two examples of a widespread doubt concerning the universality of multicultural values.How can we deal with this "multiplicity of multiculturalisms" in theory and in action?To address this complex issue, this special issue will partly build on ongoing conversations among participants and organisers of a summer school in multicultural studies, a joint program between the University of Toronto and Osaka University.Rather than trying to explain the similarities and differences between multiculturalism and coexistence by taking their values for granted, the contributors of this special issue focus on how they hang together, shape and reshape each other through acts of comparison on various levels.The first part will look at how multiculturalism and coexistence have been shaped by cultural, social and educational policies in Canada and Japan; the second part will scale down the focus of comparison from the historical to the actual.Authors of this section report and examine an experimental exchange program of Japanese students of coexistence learning multiculturalism at the University of Toronto.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.025 |
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".