Scientific rigor and credibility in the nutrition research landscape
Bibliographic record
Abstract
Scientific progress depends on the quality and credibility of research methods. As discourse on rigor, transparency, and reproducibility joins the cacophony of nutrition information and misinformation in mass media, buttressing the real and perceived reliability of nutrition science is more important than ever. This broad topic was the focus of a 2016 plenary session, "Scientific Rigor and Competing Interests in the Nutrition Research Landscape." This article summarizes and expands on this session in an effort to increase understanding and dialogue with regard to factors that limit the real and perceived reliability of nutrition science and steps that can be taken to mitigate those factors. The end goal is to both earn and merit greater trust in nutrition science by both the scientific community and the general public. The authors offer suggestions in each of the domains of education and training, communications, research conduct, and procedures and policies to help achieve this goal. The authors emphasize the need for adequate funding to support these efforts toward greater rigor and transparency, which will be resource demanding and may require either increased research funding or the recognition that a greater proportion of research funding may need to be allocated to these tasks.
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 | 0.761 | 0.918 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.034 | 0.014 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".