Law enforcement and public health collaborations and partnerships in Africa
Bibliographic record
Abstract
Though not high profile, collaborations and partnerships between law enforcement agencies (LEAs) and public health organizations do exist across Africa. Law enforcement and public health (LEPH) partnerships have been common, but not necessarily optimum, in responses to epidemics such as sexually transmissible infections, tuberculosis, and malaria, and pandemics such as HIV/AIDS and COVID-19. There are some such collaborations in responses to gender-based violence (GBV), particular socio-economic problems and challenges, counter-insurgency and terrorism (when it happens within civilian spaces), to address issues of rape, disease and death. Leadership in development of such approaches comes from a wide range: local and national governments, law enforcement agents, CSOs, regional economic bodies and United Nations agencies. They have also a wide range of success and sustainability. There are examples of excellent collaboration in partnerships with long histories and experience of working together, especially among those that have established common goals aimed at local, national and global health outcomes. However, antagonisms also exist between civil society organizations (CSOs) and LEAs, with CSOs placing blame on law enforcement agents for harms caused, and with LEAs perceiving CSOs as enemies of the state because of their work with and advocacy for the rights of culturally or politically taboo or sensitive matters, such as sex work or homosexuality. Not uncommonly, partnerships have not been formed; or where they have formed but have failed to achieve consensus and joint results, have collapsed. Much more needs to be done at all levels to achieve effective, humane and sustained joined-up responses to difficult public health issues in the African context.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".