Who is Asian? A category that remains contested in population and health research
Bibliographic record
Abstract
Continuing inconsistent use of the term 'Asian' and its appearance for the first time in the 2001 Census justifies an examination of its utility in population and health research. Given the potential for 'Asian' to describe either persons with origins in the Indian subcontinent or those originating from continental Asia, there is a strong argument in studies employing ethnicity as a measure of broad historical processes of colonialism, migration, and discrimination for privileging 'South Asian' over this contested term. Where the focus is on ethnicity as personal identity, there is some evidence of the emergence of bicultural terms such as 'Asian British' and 'Scottish Asian' and of more limited use regionally of 'Asian' and qualified terms such as 'Hindu Asian'. However, such usage cannot be generalized to the acceptance of a pan-Asian identity. Further, the different meanings that attach to terms such as 'Asian' and 'Indian' in the USA and Canada in terms of the specificity of each country's historical process of ethnogenesis mean that, where international comparisons are being made, accurate description of the population is needed to explain the terminology.
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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 | 0.047 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".