Answer Formats in British Census and Survey Ethnicity Questions: Does Open Response Better Capture ‘Superdiversity’?
Why this work is in the frame
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Bibliographic record
Abstract
During a period of unprecedented ethnicity data collection in Britain, an almost universal characteristic of this practice has been the mandated use of the decennial census ethnicity classifications. In Canada and the USA a greater plurality of methods has included open response, now recommended for the 2020 US Census. As the ethnic diversity of Britain has increased, driven by immigration dynamics and population mixing leading to ‘superdiversity’, the census is no longer able to capture the new populations. The validity and utility of unprompted open response is examined in several ‘mixed race’ datasets. It is argued that open response can be a modus operandi for large-scale ethnicity data collection and that the lack of consistency in recording of such responses need not necessarily be viewed as a drawback. Open response offers substantial insights into the country’s superdiversity in a way that ethnicity categorization alone cannot.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it