Defending Democracy and Securing Diversity
Bibliographic record
Abstract
1. Introductory Note Christian Leuprecht 2. Rethinking Diversity and Security Alan Okros 3. Evolution of Policing and Security: Implications for Diverse Security Sectors David Last 4. Evolving UK Policy on Diversity in the Armed Services: Multiculturalism and its Discontents David Mason and Christopher Dandeker 5. Harnessing Social Diversity in the British Armed Forces: The Limitations of 'Management' Approaches Victoria Marie Basham 6. Sex, Gender and Cultural Intelligence in the Canadian Forces Karen D. Davis 7. Ethnic Cultural Minorities and their Interest in a Job in the Royal Dutch Army Jelle van den Berg and Rudy Richardson 8. Can Women Make a Difference? Female Peacekeepers in Bosnia and Kosovo Liora Sion 9. Diversity in the Canadian Forces: Lessons from Afghanistan Anne Irwin 10. Ethnic Diversity and Police-Community Relations in Guyana Joan Mars 11. The Politics of Race and Gender in the South African Armed Forces: Issues, Challenges, Lessons Lindy Heinecken and Noelle van der Waag-Cowling 12. Gender Mainstreaming: Lessons for Diversity Donna Winslow 13. Diversity as Strategy: Democracy's Ultimate Litmus Test Christian Leuprecht
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".