Are Reliability, Reproducibility and Validity the Correct Terms to Assess the Correctness of Dietary Studies?
Bibliographic record
Abstract
Nutritional studies often use the terms reliability, reproducibility and validity to indicate the correctness of the study. These terms do not appear to have a universal meaning to all researchers. The components of a dietary study are the input, the data collection instrument and the compiled data. Frequently the data collection questionnaire/tool/instrument is tested for reliability, reproducibility or validity. The data collection questionnaire/tool/instrument is simply a structure, a vehicle for gathering data. An argument is presented that demonstrates the reasons that such a structure cannot be tested for reliability, reproducibility or validity. The logical approach to the use of the terms reliability, reproducibility and validity is presented. Reliability refers to the input component of the study, reproducibility may or may not lead to strengthening the study and validity refers to the truthfulness of the database generated. Validity must be derived from reliable and reproducible data.
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 | 0.331 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".