Legislating for Good Governance in the Pharmaceutical Sector through UN Convention Against Corruption (UNCAC) Compliance
Bibliographic record
Abstract
(UNCAC) compliance in seven countries and examines how full UNCAC adoption may reduce corruption risks within four key pharmaceutical decision-making points: product approval, formulary selection, procurement, and dispensing. Countries were selected based on their participation in the Medicines Transparency Alliance and the WHO Good Governance for Medicines Programme. Each country's domestic anti-corruption laws and policies were catalogued and analysed to evaluate their implementation of select UNCAC Articles relevant to the pharmaceutical sector. Countries displayed high compliance with UNCAC provisions on procurement and the recognition of most public sector corruption offences. However, several countries do not penalise private sector bribery or provide statutory protection to whistleblowers or witnesses in corruption proceedings, suggesting that private sector pharmaceutical dispensing may be a decision-making point particularly vulnerable to corruption. Fully implementing the UNCAC is a meaningful first step that countries can take reduce pharmaceutical sector corruption. However, without broader commitment to cultures of transparency and institutional integrity, corruption legislation alone is likely insufficient to ensure long-term, sustainable pharmaceutical sector good governance.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".