Addressing conflicts of interest in Public Private Partnerships
Bibliographic record
Abstract
BACKGROUND: Many articles have been written on conflicts of interests (COIs) in fields such as medicine, business, politics, public service and education. With the growing abundance of Public Private Partnerships (PPPs), often involving complex relationships among the partners, it is important to understand how COIs can be mitigated and managed in PPPs. DISCUSSION: We wanted to study PPPs, particularly in the areas of global health and agriculture, but discovered no single source of information available to identify and compare various approaches for avoiding and managing COIs in PPPs. This is a significant gap, especially for those wishing to study, compare and strengthen existing COI policies related to PPPs. In order to bridge this gap, we reviewed how PPPs currently address COIs and highlight what might be considered good practice in developing COI policies. We reviewed the online COI policies of 10 PPPs in global health and agriculture, and interviewed two global health PPP chief executives. SUMMARY: Based on our review of policies and interviews, we conclude that there exists a range of good practices including attention to accountability and governance, acknowledgement and disclosure, abstention and withdrawal, reporting and transparency, and independent monitoring. There appears to be a need for PPPs to interact closely and learn from each other on these parameters and to also place more emphasis on independent external monitoring of COIs as a means of strengthening their major social objectives on which their activities are largely predicated. We also recommend the establishment of a web based database, which would serve as a forum to discuss COI issues and how they can be resolved.
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 | 0.222 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".