Down the Rabbit Hole: How Digital Media Shapes Public Perceptions of Food Science and Technology Research (And How We Can Safeguard Science Integrity and Credibility)
Bibliographic record
Abstract
Professionals and practitioners in food science and technology navigate a minefield of challenges stemming from the convergence of science, scientific inquiry and research, and online mass and social media. Misinterpretations and politicized debates occur frequently in online media, where food- and diet-related topics have an avid following, and conflicting information or incomplete coverage may occasionally undermine public trust in the integrity of food science research from both academia and industry. Leveraging a broad landscape analysis of scientific and popular lay journal reports, we catalog a series of food science and technology topics that have been popularized in online forums, sometimes at the expense of scientific accuracy. Finally, we detail some guidelines and tools that may assist food science and technology academics, industry professionals, science publishers, and online journalists in rigorously safeguarding the integrity and credibility of research reports that reach the lay consumer through social and online media channels.
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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 | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".