A SWOT Analysis of Multiculturalism in Canada, Europe, Mauritius, and South Korea
Bibliographic record
Abstract
In this special issue on “Multiculturalism During Challenging Times,” we present six articles focused on multiculturalism as it is currently practiced or implemented in Canada, across Europe, in Mauritius, and in South Korea. We apply SWOT (strengths, weaknesses, opportunities, threats) analysis to assess the strengths and weaknesses of its application and the opportunities and threats it presents for the countries studied here. Strengths: We find that multiculturalism fosters national identity, promotes cultural tolerance and modernization, and assists with the incorporation of cultural minorities. Weaknesses: At the same time, multiculturalism also creates “faultlines” along cultural and religious groups, could promote separate and parallel lives, and could pose a challenge to equality in liberal societies. Opportunities: Multiculturalism has the potential to be used as a tool for attracting talents, a source of competitive advantage for nations, and a discourse for politicians to score political gains. Threats: Multiculturalism also has the potential to be perceived as incompatible with Western, liberal values, a burden to the state welfare, and challenge existing national identities. We conclude with some suggestions for future research to extend our understanding of multiculturalism within the context of increasing globalization and greater international migration.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".