Bibliographic record
Abstract
Local ownership is widely considered to be one of the core principles of successful Security Sector Reform (SSR) programmes. Nonetheless, there remains a gap between policy and practice. This article examines reasons for this gap, including concerns regarding limited capacity and lack of expertise, time and cost constraints, the allure of quantifiable results and quick wins, and the need to ensure that other principles inherent to SSR are not disregarded. In analysing what is meant by local ownership, this article will also argue that, in practice, the concept is narrowly interpreted both in terms of how SSR programmes are controlled and the extent to which those at the level of the community are actively engaged. This is despite policy guidance underscoring the importance of SSR programmes being inclusive and local ownership being meaningful. It will be argued that without ensuring meaningful and inclusive local ownership of SSR programmes, state security and justice sector institutions will not be accountable or responsive to the needs of the people and will, therefore, lack public trust and confidence. The relationship between the state and its people will be weak and people will feel divorced from the decisions that affect their security and their futures. All this will leave the state prone to further outbreaks of conflict. This article will suggest that the requisite public confidence and trust in state security and justice sector institutions, and ultimately, the state itself, could be promoted by SSR programmes incorporating community safety structures.
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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 | 0.013 | 0.018 |
| 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 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".