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Funding food science and nutrition research: financial conflicts and scientific integrity

2009· review· en· W2006381679 on OpenAlex
Sylvia Rowe, Nick Alexander, Fergus M. Clydesdale, Rhoná S. Applebaum, Stephanie A. Atkinson, Richard Black, Johanna Dwyer, Eric Hentges, Nancy A. Higley, Michael Lefevre, Joanne R. Lupton, Sanford A. Miller, Doris Tancredi, Connie M. Weaver, Catherine E. Woteki, Elaine Wedral

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNutrition Reviews · 2009
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsMcMaster University
FundersGeneral MillsPepsiCoCoca-Cola FoundationProcter and Gamble
KeywordsCredibilityGovernment (linguistics)Public relationsConflict of interestRemunerationPolitical scienceEngineering ethicsSet (abstract data type)LawComputer scienceEngineering

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.897
GPT teacher head0.675
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it