Monitoring HIV-Related Laws and Policies: Lessons for AIDS and Global Health in Agenda 2030
Bibliographic record
Abstract
The National Commitments and Policy Instrument (NCPI) has been used to monitor AIDS-related laws and policies for over 10 years. What can be learnt from this process? Analyses draw on NCPI questionnaires, NCPI responses, the UNAIDS Law Database, survey data and responses to a 2014 survey on the NCPI. The NCPI provides the first and only systematic data on country self-reported national HIV laws and policies. High NCPI reporting rates and survey responses suggest the majority of countries consider the process relevant. Combined civil society and government engagement and reporting is integral to the NCPI. NCPI experience demonstrates its importance in describing the political and legal environment for the HIV response, for programmatic reviews and to stimulate dialogue among stakeholders, but there is a need for updating and in some instances to complement results with more objective quantitative data. We identify five areas that need to be updated in the next iteration of the NCPI and argue that the NCPI approach is relevant to participatory monitoring of targets in the health and other goals of the UN 2030 Agenda for Sustainable Development.
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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 | 0.096 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".