Banning landmines : disarmament, citizen diplomacy, and human security
Bibliographic record
Abstract
Chapter 1 Foreword Chapter 2 Chapter 1. Banning Landmines and Beyond Part 3 Part I. Banning Landmines Chapter 4 Chapter 2. A Beacon of Light: The Mine Ban Treaty since 1997 Chapter 5 Chapter 3. Still Alive and Kicking: The International Campaign to Ban Landmines Chapter 6 Chapter 4. Evidence-Based Advocacy: Civil Society Monitoring of the Mine Ban Treaty Chapter 7 Chapter 5. Surround the Cities with the Villages: Universalization of the Mine Ban Treaty Chapter 8 Chapter 6. An Emphasis on Action: The Mine Ban Treaty's Implementation Mechanisms Chapter 9 Chapter 7. Goodwill Yields Good Results: Cooperative Compliance and the Mine Ban Treaty Chapter 10 Chapter 8. An Indispensable Tool: The Mine Ban Treat and Mine Action Chapter 11 Chapter 9. Beyond the Rhetoric: The Mine Ban Treaty and Victim Assistance Chapter 12 Chapter 10. Outside the Treaty not the Norm: Non-State Armed Actors and the Landmine Ban Part 13 Part II. Beyond Landmines Chapter 14 Chapter 11. Citizen Diplomacy and the Ottawa Process: A Lasting Model? Chapter 15 Chapter 12. Unacceptable Behavior: How Norms are Established Chapter 16 Chapter 13. Cluster Munitions in the Crosshairs: In Pursuit of a Prohibition Chapter 17 Chapter 14. Nothing About Us Without Us: Securing the Disability Rights Convention Chapter 18 Chapter 15. Tackling Disarmament Challenges Chapter 19 Chapter 16. New Approaches in a Changing World: The Human Security Agenda Chapter 20 Appendix. 1997 Mine Ban Treaty and Its Status
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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 | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".