Bibliographic record
Abstract
identities affect our ability to make an individual life and have relevance in the context of suicide risk.The investigators exclude 'Other Asian' (the free-text 'Any other Asian background' under the 'Asian or Asian British' label, numbering around 240 000 in the 2001 census) from the denominator 'because the majority of this group are of Middle Eastern or Sri Lankan origin'.Although around one in four were born in Sri Lanka and one in six in the Middle East, 37% had a region of birth in South Asia and 31% in the UK. 4 Given that the focus is on ethnicity rather than country of birth, the ONS Longitudinal Study data are, again, informative: of members with a 1991 and 2001 ethnic group, 42% of 1285 'Other Asian' persons identified as Indian, Pakistani or Bangladeshi in 1991.In this study, none from the 'Other Asian' group are counted in the denominator.Finally, the investigators point out that SANGRA was validated against real data.However, the key data-set were London and Midlands hospital in-patient admission data from the mid-to late-90s, a period during which the quality of ethnic coding was very poor, the team itself admitting that further studies are needed to confirm whether SANGRA is able to produce valid results across Britain.5 Beyond the parsimonious way in which the statistical data is presented (with no measure of the precision of the rate estimates), the collective effect of potential problems with numerator/ denominator compatibility and concerns about SANGRA's performance is a factor which needs to be considered in making a judgement whether to accept these findings as the accurate contemporary evidence needed to shape specific prevention strategies.Asian origin in England
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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 | 0.007 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.038 | 0.037 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".