Buying social justice : equality, government procurement, and legal change
Bibliographic record
Abstract
1. What is this book about? PART I: PRELIMINARIES 2. Roots 3. Status Equality Law and Policy 4. International and European Procurement Regulation 5. Buying Social Justice? PART II: THE WORLD TRADE ORGANIZATION AND PROCUREMENT LINKAGES 6. Contract compliance in the United States and Canada 7. Set-asides in the United States, Canada 8. Evolution of the Government Procurement Agreement Model and procurement linkages 9. Procurement linkages and developing countries PART III: EQUALITY LINKAGES AND THE EUROPEAN COMMUNITY 10. Procurement linkages and the 1980s reform of EC procurement regulation 11. Domestic procurement linkages during the 1990s and the chilling effect of European procurement regulation 12. Changing approaches to procurement linkages in the Community and beyond 13. Expansion of equality linkages in the Member States 14. Procurement linkages and the 2003 legislative reforms: a modus vivendi in sight? PART IV: INTERPRETATION 15. Interpreting the Government Procurement Agreement 16. EC public procurement law and equality linkages: foundations for interpretation 17. European public procurement law and equality linkages: government as consumer, government as regulator PART V: CONCLUSIONS 18. Reconciling social and economic approaches to public procurement
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".