Ethnic and cultural diversity in Europe: validating measures of ethnic and cultural background
Bibliographic record
Abstract
Socio-cultural and ethnic origin can be a powerful predictor of social attitudes and behaviours but, unlike the situation in the classical countries of immigration such as Australia, Canada and the USA, there is no standard measure in Europe for measuring ethnic background. The paper reports a new measure and classification, developed for the ESS and trialled in the ESS wave 7 (2014/2015). It describes the underlying theoretical concepts, structure and classification criteria and reports a range of substantive findings. The paper shows that the new measure of ethnic origins has both criterion and predictive validity: it predicts whether respondents identify themselves as belonging to an ethnic minority and whether they feel that theirs is a group which is discriminated against. It also predicts strength of national identity and attitudes towards immigration. A particular strength of the new measure is that it identifies both indigenous and (sub)national minorities as well those with a migration background. The paper shows that in some countries subnational minorities are quite distinctive, for example in their feelings of being discriminated against and in their low levels of national attachment.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".