Immigration has completely changed the city. There’s a thriving community of Asian people.
Bibliographic record
Abstract
I was born in Toronto, Canada, but grew up in Birmingham and have lived here my whole life. When I was growing up, the area had a lot of elderly white British people. Immigration has completely changed the city. There’s a thriving community of Asian people. They’ve built up shops and restaurants and there’s a particular energy in the streets. Most recently, there has been a growth in Eastern European communities, who have come to Birmingham to work. As I kid I went to quite a white school in my neighbourhood, so I used to get people saying ‘Paki this, Paki that’. I got used to it very quickly, so it never used to bother me too much. It doesn’t happen so often any more. I feel integrated within the community, as we have all grown up together and we’ve known each other for years. That’s now starting to change; people are moving outside of the area, to build homes, in quieter neighbourhoods or outside of Birmingham. My dad started Thandi Coaches and I’ve been working in the family business for 20 years. It was set up initially to service the Asian communities in the UK – typically, between areas such as Southall, Birmingham and Bradford. I feel that Britain feels like a worse place today than when I was growing up. There’s too much crime in the streets. Young kids, just 16 to 18, carrying knives, hanging out in gangs. There’s been an increase in armed robberies; they just grab anything they can for free. As a result of the cutbacks to the police, we have less of them servicing our community, and they aren’t quick enough to respond to crime. It’s sad, it’s become a mindset for these young people and it happens because of a lack of opportunity for them as they are growing up. I think we’ve had too much immigration in this country for too long. We need to make sure we can help and provide for our own communities in the UK. Of course, it’s horrible what’s happened to the people affected by the Windrush scandal – these people have worked all their lives, settled down with families, built homes, and this is how they’ve been treated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.030 |
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".