The Imperative to Explore the Impact of Disarmament on Peacemaking Efforts and Conflict Recurrence
Bibliographic record
Abstract
Abstract There is today a well-established consensus that belligerents must be disarmed in order to reconstruct shattered states and establish a robust and durable peace in the wake of internal armed conflict. Indeed, nearly every UN peacekeeping intervention since the end of the Cold War has included disarmament provisions in its mandate. Disarmament is guided by the arrestingly simple premise that weapons cause conflict and, therefore, must be eradicated for a civil conflict to end. If the means by which combatants fight are eliminated, it is thought, actors will have little choice but to commit to peace. Disarmament is, therefore, considered a necessary condition for establishing the lasting conditions for peace. To date, however, no systematic quantitative analysis has been undertaken of the practice of disarmament and the causal mechanisms remain underspecified. This paper is a preliminary attempt to fill that gap. In it we outline a series of hypotheses with which to run future statistical analyses on the effects of disarmament programs. The success of negotiations and the durability of peace are, perhaps, the single most salient issues concerning those engaged in conflict termination efforts. We therefore focus the bulk of this paper on a review of the supposed effects of disarmament on negotiating outcomes and war recurrence.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".