The gluten lie: And other myths about what you eat by Alan Levinovitz
Bibliographic record
Abstract
What nutrition buzzword is on the tip of more tongues than gluten? Today’s popular obsession with gluten, or gluten avoidance more precisely, has spurred a bevy of gluten-free products and cookbooks with recipes for items such as cauliflower pizza crust. The Canadian market for gluten free products grew 26% between 2008 and 2012, and the sales for gluten-free foods in Canada has been estimated at upwards of $460 million despite the relatively low numbers of Canadians who require gluten free foods due to a diagnosis of Celiac disease (1%) or non-celiac gluten sensitivity (6%) (Agriculture and Agri-foods Canada, 2014). The most common reasons to avoid gluten given by those without a medical need to do so include “digestive health,” “nutritional concerns,” and “weight loss” (Agriculture and Agri-foods Canada, 2014). Calling Alan Levinovitz’s book, The Gluten Lie: And Other Myths About What You Eat timely is an understatement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".