Many authors of publicly available top-selling nutrition books in Canada are without clinical nutrition credentials, do not cite evidence, and promote their own services or products
Bibliographic record
Abstract
The accuracy of books as public nutrition resources varies substantially; whether authors of publicly available nutrition books possess related experience, cite scientific evidence, or have other financial incentives has not been assessed thoroughly. This study aimed to determine if publicly available top-selling nutrition books are written by authors who (1) have relevant expertise, (2) cite scientific evidence, and (3) benefit financially in other ways. Best-selling nutrition books were gathered from Amazon Canada. Differences in scientific citations and financial incentives were compared between authors with and without credentials. Authors who were Doctor of Medicine (MD), registered dietitians (RD), chiropractors, or naturopathic doctors had more in-text citations (56% versus 25%; p = 0.014) and cited more scientific articles (83% versus 50%; p = 0.0045) compared to all other authors. The majority of authors of publicly available top-selling nutrition books in Canada did not have MD/RD credentials. Many of the authors promoted their own services or products, regardless of credentials.
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 | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.026 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".