A sensitive question? The effect of an ethnic background question in surveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nearly half of European countries choose not to collect data on their citizens' ethnicity. One of the reasons is related to ethnic background being, potentially, a sensitive issue. We explore this social sensitivity in relation to questions asking about respondents' ethnic background through a survey experiment that compares two multicultural and liberal democracies with different traditions related to the collection of data on residents' ethnic background: Sweden and Canada. The findings demonstrate that, when the ethnicity question produced a significant effect on survey evaluations, it was always positive. Similar results were found in both countries. Thus, we conclude that—in terms of survey methodology and the reaction of individuals to such questions—there does not exist a valid justification to omit questions on ethnic background from surveys or censuses in pluralist societies.
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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 | 0.008 | 0.001 |
| 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