Safeguarding the Integrity and Credibility of Food Science Research: Navigating Challenges as Professionals
Bibliographic record
Abstract
Objectives: Professionals and practitioners in food science & technology navigate a minefield of challenges stemming from the convergence of scientific inquiry and research, and mass and social media. Escalating skepticism and erosion of trust in science is exacerbated by poorly conducted science, plagiarism, inadequate peer review, predatory publishing, misrepresentation of science in the media, and public perceptions of science that are shaped by politicization and mis- and disinformation. Methods: Key search terms (science, food science & technology, nutrition) were crosslinked with search terms that describe challenges undermining trust in science (media, mis/disinformation, skepticism, hype, generative AI, credibility, politicization, etc.). Over 200 articles covering social media impacts on scientific credibility, the evolution of science & peer review, the rapidly changing rules governing scientific output in academia and industry and codes of ethics meant to govern how professionals work, particularly in the context of food science, food technology, and nutritional science. Results: Contradictory 'facts' presented in mass and social media generate distrust in scientific discoveries. Leveraging the comprehensive literature review, a strategic framework was defined that 1) identifies and manages factors that challenge integrity and credibility of food research, and 2) prescribes strategies that allow professionals to mitigate and manage challenges in this complex space in order to provide credible research results in food & nutritional science and food technologies. Evidence supports a compelling need for strict adherence to common codes of ethics when conducting, reporting and communicating research results in academia and public forums. Conclusions: Tools and a framework for professionals identifies the intersection of factors that contribute to erosion of trust and highlights challenges as they relate to perceived loss of integrity or credibility in food science and technology. Funding Sources: N/A.
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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 | 0.425 | 0.580 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.052 | 0.055 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".