Bibliographic record
Abstract
Within the repertoire of international stabilization interventions, security sector reform (SSR) and other conventional efforts to strengthen security and governance institutions remain central. There is increasing recognition that the policies and practices operating under the rubric of SSR are blind to the empirical reality of security pluralism in most stabilization contexts. In these contexts, both security providers directly authorized by the state (police, army) and a multitude of other coercive actors engage in producing and reproducing order, and enjoy varying degrees of public authority and legitimacy. Recognizing this, research was undertaken in three cities (Beirut, Nairobi, and Tunis) to discern the conditions enabling various security providers to forge constructive relations with local populations and governance actors. Drawing on insights generated by these case studies, this article problematizes conventional state-centric approaches and argues for a bold reimagining of SSR. It makes the case for an SSR approach that prioritizes promoting the accountability and responsiveness of all security providers, integrating efforts to strengthen the social determinants of security, and enabling a phased transition from relational to rules-based systems of security provision and governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.100 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| 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".