Bibliographic record
Abstract
Introduction The Labour Party 2005 General Election manifesto summarised the key elements of its approach to community safety under the heading ‘Safe communities, secure borders’. Citing its record to date, it also set out its view of the issues it would continue to prioritise. These linked measures to address antisocial behaviour and reduce crime with the need to tackle illegal immigration and the threat of terrorism. We are giving the police and local councils the power to tackle antisocial behaviour; we will develop neighbourhood policing for every community and crack down on drug dealing and hard drug use to reduce volume crime; we are modernising our asylum and immigration system; and we will take the necessary measures to protect our country from international terrorism. (Labour Party, 2005, p 42) This chapter explores the implications of these commitments for ethnic minorities in Britain. It starts by charting the development of ethnic diversity, not least in order to unpack some of the complexities often neglected in discussion and largely ignored in available statistics. Next, evidence regarding the effect of ‘community safety’ issues on the two largest minority ethnic groups up to the election of the New Labour government of 1997 is reviewed as background to developments since then. In the light of this, the impact on minorities of the government's current approach to community safety is considered. The chapter concludes by discussing the implications of continuing to pursue this approach in future. Ethnic minorities in Britain Most discussion of ethnic minorities’ experience in Britain as victims of crime (and as offenders) has focused on groups with origins in the migration to Britain from its former colonies following the Second World War. Only in 1962 were restrictions first imposed on Commonwealth citizens coming to Britain; and, although progressively tightened from 1968 onwards, they were applied selectively. Citizens of ‘Old Commonwealth’ countries (mainly Canada, New Zealand and Australia) were largely unaffected until rights of entry were reframed in the new Nationality Act of 1981. The main targets for control were people from the ‘New Commonwealth’; and the largest numbers of these in the post-war period had come first from countries of the Caribbean in the 1950s and 1960s.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".