Unruly Reform: Explaining Diversion in Local Security Governance Rules in Nepal
Bibliographic record
Abstract
What factors enable, constrain or distort the implementation of legal reform governing policing? The legal architecture determines who holds authority to influence security decisions, from broad strategic directions to day-to-day implementation. Yet rules will be interpreted or even ignored by actors entrusted with their implementation, while unauthorised actors can be allowed to exert influence. Can we account for these distortions when reforming security sector governance to make security more inclusive? This case study of institutional change in Nepal analyses the forces that distorted the effect of a change in formal rules on the local governance of security. It shows that, while changes did occur around who was able to influence policing decisions, these were mediated at the local level by habits and different notions of legitimacy. Local governance reforms had a greater impact on local security governance than constitutional provisions endowing provincial governments with formal authority over security governance. This study underlines the importance of considering multi-tiered dynamics when analysing the governance of security. For security sector reform practitioners and policymakers, the study suggests a need to broaden the focus from rules about security to rules with an impact on security governance. One should also explicitly address the fate of existing institutions in reform processes, if they are meant to be replaced by a new architecture.
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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 | 0.002 | 0.007 |
| 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 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".