Combating racial discrimination : affirmative action as a model for Europe
Bibliographic record
Abstract
In Europe as well as in other parts of the world, xenophobia and racism are among the unsolved problems of the ending 20th century. Globalization, mass migration and unemployment as well as the need to invent new supra- or crossnational identities require new political answers concerning the problems of inclusion and exclusion.In the United States and in Canada, 'affirmative action' programmes are among those policies which are intended to redress the injustice of discrimination based primarily on race, ethnicity, sex, but also on national origin, religion, or disability.This timely book is the first to present an overview of these hotly debated questions and the anti-discrimination policies in different countries. Experts from the United States, Canada and Europe examine the historical, institutional, judicial and sociological conditions of affirmative action and look at shifting concepts of racism, equality, integration and assimilation. They address the vital questions of whether policies originally created to increase opportunities for African Americans can be applied in Europe; whether the primary goal of 'affirmative action' should be to correct injustice or to safeguard diversity; and whether the democratic ideal of individual equality is at odds with what many perceive as preferential treatment. Moral success but political failure? Compensatory justice or reverse discrimination? This important book evaluates more than thirty years of affirmative action and helps to develop new instruments to deal with the roots and the effects of discrimination.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".