From conflict to partnership: growing collaboration between police and NGOs in countries with concentrated epidemics among key populations
Bibliographic record
Abstract
INTRODUCTION: Between September 2012 and December 2015, a series of national and regional consultations, aimed at resolving a persistent dynamic of conflict between law enforcement agencies (LEAs) and civil society organizations (CSOs) working on issues of access to HIV services in high-priority countries for people who use drugs have been organized by the HIV/AIDS Section of the United Nations Office on Drugs and Crime, the Joint United Nations Programme on HIV/AIDS, the Law Enforcement and HIV Network (LEAHN) and other international organizations. The aim of these consultations has been to understand, at a national and regional level, the key points of tension between police and CSOs and how to overcome these tensions to enhance access to and uptake of services by key populations, including people who inject drugs, sex workers, men who have sex with men and transgenders. This commentary briefly describes the methods, process, content and key outcomes of these consultations held across diverse number of countries and regions, including Africa, South East Asia, South Asia, Central Asia, Eastern Europe and Latin America. DISCUSSION: While the context varies, this paper highlights that there are commonalities that drive a persistent dynamic of conflict and therefore also common methods for resolution of conflict and forging partnerships. Both policing and CSOs have key sectoral responsibilities and reform agendas to implement to ensure that as an individual agency they are able to meet their obligations as partners in the HIV response. Using the key outcomes of discussions and recommendations from these consultations and drawing on existing literature, the objective of this paper is to present a preliminary model that roadmaps the critical path from resolution of conflict to partnership between LEAs and CSOs. CONCLUSIONS: This paper seeks to highlight that critical resources are required to support ongoing development and harnessing of partnerships between LEAs and CSOs and argues that these resources should not just come from global HIV funding mechanisms but should be part of a more mainstreamed security sector reform agenda that understands the mutual benefits that programming for human rights-based policing reform would have on HIV, development and security.
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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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".