Moving the debate forward: interculturalism’s contribution to multiculturalism
Bibliographic record
Abstract
In this article, we compare Ricard Zappata-Barrero's interculturalism with Tariq Modood's multiculturalism. We will discuss the relation between distinct elements that compose both positions. We examine how recent discussions on interculturalism have the potential to contribute to theories of multiculturalism without undermining their core principles. Our position is close to that of Modood's as he has already carefully tried to incorporate interculturalist insights into his own multiculturalism. Yet we provide a raise a few questions regarding Modood's treatment of the relation between multiculturalism and interculturalism. After summarizing each author's potion (I), we will comment on the following set of relations between their basic elements: (II) The relation between intercultural contact and intercultural dialogue; (III) The relation between contact at the local level and the societal/state level; (IV) The relation between group-specific measures, intercultural contact and mainstreaming.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".