Multicultural conversations: The nature and future of culture, identity and nationalism
Bibliographic record
Abstract
Despite well-known criticism of multiculturalism in Britain, the Netherlands, Germany, Canada, Australia, India and elsewhere since 9/11, such policies have proliferated ( Banting and Kymlicka, 2013 ; Mathieu, 2018 ) and the Canadian and Australian policies of multiculturalism have since celebrated their 50th birthdays. Political theories of multiculturalism have proliferated in this period too ( Lenard, 2022 ; Modood, 2007/2013 ; Patten, 2014 ; Parekh, 2006 , 2019 ; Phillips, 2007 ; Tyler, 2011 ). Schools of multiculturalist thought have been identified ( Levey, 2019 ; Uberoi and Modood, 2019 ), as have contextual methods in the political theory and normative sociology of multiculturalism ( Modood, 2020 ; Modood and Thompson, 2018 ). New historical inquiries into the origins of the political thought of multiculturalism have begun ( Tyler, 2017 ; Uberoi, 2021 ) and the ideas of multiculturalists have been altered to defend majority rights ( Koopmans and Orgad, 2022 ). Current and former politicians continue to debate its merits ( Braverman, 2023 ; Denham, 2023 ). Policies of multiculturalism and multiculturalist ideas have thus proved more resilient than many had thought. In the following conversation chaired by James Connelly, which took place on 20 June 2023, Bhikhu Parekh, Tariq Modood, Varun Uberoi, and Colin Tyler discuss the history, varied natures, and future of the contested multiculturalist ideas of “culture,” “identity” and “nationalism”.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.054 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".