Bibliographic record
Abstract
Western countries have become increasingly diverse in recent decades and these demographic trends are certain to continue. The resulting ethnic diversity is a major challenge to policy-makers, who need to tackle issues of social justice and social integration. Education plays a pivotal role since it is the major stepping stone for the children of immigrants to successful economic integration and also plays a major role in social and political integration more generally since education gives access to the skills, resources and contacts which enable individuals to participate fully in the life of their society. Our central research questions are: Do the descendants of migrants experience equality of educational opportunity relative to their peers from the majority population in their country of residence? Do minorities experience ‘ethnic penalties’ in Western educational systems in addition to the social class disadvantages which we know to be pervasive? Are some minority groups are more successful than others? And do some national contexts provide more favourable conditions for achieving equality of opportunity and avoiding ethnic penalties? The chapters describe the extent to which minorities experience inequality of opportunity in ten Western countries (Belgium, Canada, England and Wales, Finland, France, Germany, the Netherlands, Sweden, Switzerland and the USA) and examine whether disadvantages cumulate or are mitigated across the educational career as a whole. We explore reasons why the children of migrants seem to make greater progress in some countries than others, focusing on the extent to which their parents were ‘positively selected’ and on the nature of each country's educational systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".