Funding food science and nutrition research: financial conflicts and scientific integrity
Bibliographic record
Abstract
There has been significant public debate about the susceptibility of research to biases of various kinds. The dialogue has extended to the peer-reviewed literature, scientific conferences, the mass media, government advisory bodies, and beyond. While biases can come from myriad sources, the overwhelming focus of the discussion, to date, has been on industry-funded science. Given the critical role that industry has played and will continue to play in the research process, the International Life Sciences Institute (ILSI) North America Working Group on Guiding Principles has, in this paper, set out proposed conflict-of-interest guidelines, regarding industry funding, for protecting the integrity and credibility of the scientific record, particularly with respect to health, nutrition, and food-safety science. Eight principles are enumerated, specifying ground rules for industry-sponsored research. The paper, which issues a challenge to the broader scientific community to address all bias issues, is only a first step; the document is intended to be dynamic, prompting ongoing discussion and refinement. The Guiding Principles are as follows. In the conduct of public/private research relationships, all relevant parties shall: 1) conduct or sponsor research that is factual, transparent, and designed objectively; according to accepted principles of scientific inquiry, the research design will generate an appropriately phrased hypothesis and the research will answer the appropriate questions, rather than favor a particular outcome; 2) require control of both study design and research itself to remain with scientific investigators; 3) not offer or accept remuneration geared to the outcome of a research project; 4) prior to the commencement of studies, ensure that there is a written agreement that the investigative team has the freedom and obligation to attempt to publish the findings within some specified time-frame; 5) require, in publications and conference presentations, full signed disclosure of all financial interests; 6) not participate in undisclosed paid authorship arrangements in industry-sponsored publications or presentations; 7) guarantee accessibility to all data and control of statistical analysis by investigators and appropriate auditors/reviewers; and 8) require that academic researchers, when they work in contract research organizations (CRO) or act as contract researchers, make clear statements of their affiliation; require that such researchers publish only under the auspices of the CRO.
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 | 0.266 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.034 | 0.015 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".