Principles for building public-private partnerships to benefit food safety, nutrition, and health research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it