What Can Multicultural Theory Tell Us about Integrating Muslims in Europe?
Bibliographic record
Abstract
Across Europe and elsewhere we are witnessing a series of decisions, including attempts (successful and otherwise) to ban the burka or the building of minarets on mosques, which seem to be made from a fear of ‘politicised Muslims’ – Muslim residents and citizens in European countries – who are alleged to be irredeemably and unambiguously anti-European and therefore anti-democratic. They are, additionally, frequently portrayed as taking orders from fundamentalist regimes, or organisations, which operate from outside Europe and aim to destabilise European political life. The debates and conflicts portrayed in the Swiss media in the week before the vote on whether to ban the construction of minarets, for example, and in the days just afterwards, mirror those that have been occupying European countries for the past several years. As I shall suggest in this article, these debates are familiar to theorists of multiculturalism, and of liberal democracy more generally, and they press us to consider whether the challenges (apparently) posed by Muslim integration are distinct from the challenges that have occupied the attention of multicultural theorists in the past. Underpinning the analysis in this article is my view that the principles that underpin multicultural theorising, principles that were developed in response to previous waves of immigration, are able to guide us in developing fair terms of integration for Muslim citizens.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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".