Bibliographic record
Abstract
Security sector reform (SSR) and small arms and lights weapons (SALW) reduction and control programmes have become staples of peacebuilding policy and practice in fragile, failed and conflict-affected states (FFCAS). There is wide agreement in the peacebuilding field that the two areas are intricately interconnected and mutually reinforcing. However, this consensus has rarely translated into integrated programming on the ground. Drawing on a diverse set of case studies, this paper presents a renewed argument for robust integration of SSR and SALW programming. The failure to exploit innate synergies between the two areas in the field has not merely resulted in missed opportunities to leverage scarce resources and capacity, but has caused significant programmatic setbacks that have harmed wider prospects for peace and stability. With the SSR model itself in a period of conceptual transition, the time is ripe for innovation. A renewed emphasis on integrating SSR and SALW programming in FFCAS, while not a wholly new idea, represents a potential avenue for change that could deliver significant dividends in the field. The paper offers some preliminary ideas on how to achieve this renewed integration in practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".