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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".