Part of the Solution: Exploring Armed Non-State Actor Commitment to and Compliance with an Anti-Personnel Landmine Ban
Bibliographic record
Abstract
Existing international legal arms control regimes exclude the participation of armed non-state actors, creating a situation where they cannot independently commit to those regimes, experience no sense of legitimate obligation where they do formally apply, face no credible group-level enforcement mechanism, and are not rewarded for compliance.Yet in some cases armed non-state actors do willingly make unilateral arms control commitments.One example is the 54 armed non-state actors who have, in parallel to the Ottawa Treaty regime, renounced anti-personnel landmines through a formal and monitored mechanism created by the international non-governmental organization Geneva Call.Why have some armed non-state actors committed themselves to and complied with this total anti-personnel landmine ban that generally exceeds their preexisting legal obligations?This study explains why and under what conditions armed non-state actors make this commitment.Employing a mixed research methods approach that utilizes statistical analysis of all armed non-state actors engaged by Geneva Call prior to February 2019 on the issue of anti-personnel landmines and four critical case studies, the research demonstrates how armed non-state actors have instrumentalized their commitment to the Geneva Call mechanism as a conciliatory signal to credibly convey their good faith intent toward and interest in a negotiated settlement.The findings expand and enrich previous research on armed non-state actors and highlight how bilateral peace negotiations may contribute to humanitarian commitments that mitigate violent impact on civilians even if a final peace agreement is not readily accomplished.Further, it identifies a window of opportunity for securing this commitment to anti-personnel landmine renunciation and recommends investment in the capacity needed to capitalize on these moments.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".