Principles for building public-private partnerships to benefit food safety, nutrition, and health research
Bibliographic record
Abstract
The present article articulates principles for effective public-private partnerships (PPPs) in scientific research. Recognizing that PPPs represent one approach for creating research collaborations and that there are other methods outside the scope of this article, PPPs can be useful in leveraging diverse expertise among government, academic, and industry researchers to address public health needs and questions concerned with nutrition, health, food science, and food and ingredient safety. A three-step process was used to identify the principles proposed herein: step 1) review of existing PPP guidelines, both in the peer-reviewed literature and at 16 disparate non-industry organizations; step 2) analysis of relevant successful or promising PPPs; and step 3) formal background interviews of 27 experienced, senior-level individuals from academia, government, industry, foundations, and non-governmental organizations. This process resulted in the articulation of 12 potential principles for establishing and managing successful research PPPs. The review of existing guidelines showed that guidelines for research partnerships currently reside largely within institutions rather than in the peer-reviewed literature. This article aims to introduce these principles into the literature to serve as a framework for dialogue and for future PPPs.
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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 | 0.201 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".