Anti-Weapons Activism and Arms Bans: Why Brazil Banned Anti-Personnel Mines but Not Cluster Munitions
Bibliographic record
Abstract
Abstract The transnational campaign against anti-personnel mines (APMs) succeeded in prompting their widespread ban, formalized in the 1997 Ottawa Convention. However, other anti-weapons campaigns have been less successful. What explains this difference? This article examines why Brazil banned APMs but not cluster munitions, drawing on archival sources from Brazil's Ministry of Foreign Affairs and semi-structured interviews. Adjustment costs—as perceived by coalitions of diplomats and senior military personnel—were critical in shaping Brazil's positions. They considered the costs of banning APMs negligible but perceived substantial costs of banning cluster munitions. The article also presents a decision-making model of how the relative importance of material and normative factors may vary across different stages of a decision-making process. For activists, middle powers, and small states advocating arms bans or stricter arms control norms, this study underscores the primacy of material factors.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".