Minorities and Migrants: ‘models’ of multiculturalism in Europe and Canada
Bibliographic record
Abstract
In order to understand multiculturalism, both national and immigrant minorities have to be considered. Societies are normally plural, what means that they are multi-ethnic, multi-linguistic, multi-religious, before the project of constructing a nation-State produces cultural internal homogenisation. Any debate on multiculturalism must take into account this “typical European invention” represented by nations and nation-states. The main character of the nation-state consists in a specific way to deal with internal diversity and foreign countries Nations and nation-states show a great ambivalence in relationship to diversity: on one side, nation-states exasperate differences at the “horizontal” level that means, among them (opposing strongly to the other nation-states beyond the borders); on the other side, they want to eliminate all differences at the “vertical” level, that is, internal diversity. The distinction that Will Kymlicka makes, between multicultural societies that are “multinational” -because of national minorities- and societies, which are “polyethnic” -because of immigrants- (, is extremely useful to question the usual politics of the nation-states in front of differences, that is their attempts to build linguistic and cultural homogenisation. This approach allows the rejection of the simplistic discourse that considers “cultural difference” as the barrier to immigrant minorities’ integration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".