Resentment and Multiculturalism: Kymlicka’s Canada, Bonilla Maldonado’s Colombia and Modood’s UK
Bibliographic record
Abstract
Multiculturalism is linked to theoretical approaches and social policies, which aims to encourage active interaction between groups and individuals in a given society. Multiculturalism has been theorized by the Canadian thinker Will Kymlicka who fosters a liberal democratic approach linked to Human Rights. In this article, Kymlicka’s approach is compared with Daniel Bonilla Maldono and Héctor Alonso Moreno Parra, two Colombian researchers who were influenced by Kymlicka and who are analysing the Colombian multicultural Constitution. Kymlicka is also compared with Tariq Modood who contextualizes multiculturalism within Islamic minorities in the UK and considers the impact of resentment in the dynamic of migration. Last but not least, the Canadian Doug Saunders presents the situation of migrants in slums and suburbs and emphasizes the relationship between economic well-being and multiculturalism. This leads us to contextualize these diverging perspectives on multiculturalism within the framework of a Girardian conception of human relations based on conflict and scapegoating. While comparing these different perspectives, the article underscores the fact that, until now, most thinkers did not sufficiently consider the impact of economic integration and the difference in educational credentials on the dynamics of recognition within the contemporary globalized knowledge-based society.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".