Multiculturalism is Dead: Long Live Community Cohesion? A Case Study of an Educational Methodology to Empower Young People as Global Citizens
Bibliographic record
Abstract
This article explores the theme of the ‘two faces of education’ by reviewing new policy directives in the United Kingdom to strengthen community cohesion in schools and their communities. These directives have resulted from growing disaffection with the aims and outcomes of multiculturalism. This article will investigate the ways in which this disaffection has resulted in both ‘quick fix’ politicised solutions, and in more genuine attempts to support young people to develop positive relationships with people from different ethnic backgrounds. It will suggest that whilst inequalities of educational outcome for different ethnic groups persist, schools will continue to be part of the problem, hence the second link with the theme of two (or more?) faces of education. In order to become part of the solution, schools internationally will need to adopt much more creative and complex approaches to the reduction of racism and inequality than those currently being proposed by the UK Government. A case study of an approach that has been used in many countries of the world, including Brazil and Canada, to engage young people in open dialogue, and to develop empathy and critical thinking is provided. The case study from a multi-ethnic college setting within the Midlands, United Kingdom, will illustrate how young people can be enskilled and empowered to consider key debates that have relevance to their lives as global citizens living in a culturally diverse community.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| 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".