Debating affirmative action : conceptual, contextual, and comparative perspectives
Bibliographic record
Abstract
1. Justifying Affirmative Action: Perception and Reality. (Aileen McHarg, Donald Nicolson). 2. Positive Action for Women in Employment: Time to Align with Europe? (Noreen Burrows, Muriel Robison). 3. Affirmative Action in Women's Employment: Lessons from Canada. (Nicole Busby). 4. Affirmative Action: A German Perspective on the Promotion of Women's Rights with Regard to Employment. (Anke J. Stock). 5. Widening Participation and Higher Education. (Lois S. Bibbings). 6. Preferential Treatment, Social Justice, and the Part-time Law Student - The Case for the Value-added Part-time Law Degree. (Andrew M. Francis, Iain W. McDonald). 7. Affirmative Action in the Legal Profession. (Donald Nicolson). 8. Rethinking the Merit Principle in Judicial Selection. (Kate Malleson). 9. Quotas for Women! The Sex Discrimination (Election Candidates) Act 2002. (Aileen McHarg). 10. Minority Business Enterprise Programmes in the United States of America: An Empirical Investigation. (Martin J. Sweet). 11. Is There a Duty to Legislate for Linguistic Minorities? (Robert Dunbar)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.149 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".