Bibliographic record
Abstract
The population of ethnic minorities in Britain has rapidly increased over the last 60 years. The census count indicates that the ethnic population has grown from 3 million in 1991 to 4.6 million in 2001. Issues surrounding ethnic minorities have duly been concerned with education, employment and housing. In 2001, civil unrest erupted in England’s northern mill towns. The inquiries concluded that white and British Asian communities were living parallel lives. This was seen to be a failure within the communities and of social policy. Segregation was cited as a contributory factor. Moreover, in 2005, Trevor Phillips, the chairman of the Commission for Racial Equality, warned that Britain was sleepwalking into racial segregation, with white, black and British Asian ghettos dividing cities. To tackle the segregation problem, central government introduced the community cohesion policy with the aim of developing a better understanding of shared values between all origins of race, thereby celebrating ethnic diversity in Britain. The aims of this research were to consider whether British Asian communities are segregated and to examine the viability of current central government policy in promoting and securing greater community cohesion. Oldham in Greater Manchester was selected as the focus of the investigation. This research shows that the causes of segregation, in the case study of Oldham, are clearly identified in four key areas. Firstly, historical events over the last 60 years have influenced and shaped the development of segregation between different groups, namely British Asian. Secondly, the economic and social transitions brought about by central government have been instrumental in bringing about segregation. Thirdly, local political control has further contributed to Oldham’s segregation. Finally, participants involved in this research were highly skeptical towards the community cohesion policy introduced in Oldham.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".