The failure of state multiculturalism in the UK? An analysis of the UK’s multicultural policy for 2000–2015
Bibliographic record
Abstract
If the 1997 New Labour’s winning election seems to correlate with an upsurge in both the political arena and in public favour for multiculturalism in the UK, the overall decade and a half that ensued took the very opposite path. For example, Prime Minister David Cameron declared in 2011 that state multiculturalism was a failure. In this article, I question the impact of such declarations onto the UK’s immigrant multicultural policy. In particular, using and updating the Multicultural Policy Index, I show evidence of the evolution, between 2000 and 2015, of the UK’s multicultural policy. In turn, this provides a satisfactory framework for having a clear understanding of the public policy dynamic in matters of multiculturalism in the following of David Cameron’s declarations concerning the failure of state multiculturalism. Then, echoing Meer and Modood’s argument of a ‘civic-thickening’ for the UK’s integration policy, I discuss citizenship education programs of the four constituent nations of the UK – where such integration policies have been implemented. This shows that while such curriculums all put forward approaches for ‘thickening’ togetherness, it is nonetheless consistent with a ‘multiculturalist advance’. Hence, one must invalidate the thesis following which multicultural policy and integration policy should be understood through the strict prism of a zero-sum game.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".