What Happens When We Don’t Count Women: The Hidden Hormonal Toll of the Modern Diet
Bibliographic record
Abstract
Ultra-processed foods (UPFs) now make up nearly half of the average Canadian diet. Cheap, convenient, and nutrient-poor, they not only displace whole foods but also deliver additives, preservatives, and packaging chemicals that disrupt hormones, especially in people with ovaries. These endocrine-disrupting compounds are linked to earlier puberty, menstrual irregularities, infertility, worsened menopause symptoms, and heightened risks of hormone-related diseases. Conditions like polycystic ovary syndrome and endometriosis, already affecting millions, may be exacerbated by UPF-driven inflammation and chemical exposures. Yet nutrition policy rarely addresses hormonal health, and research has long excluded women, leaving major knowledge gaps. Canada’s limited measures contrast with stronger regulations abroad, while low- and middle-income countries face a double burden of chronic disease and weak protections. The article calls for inclusive research, stronger policies, practical hormone-supportive dietary advice, expanded education, and equitable access to whole foods. Food choices are a feminist health issue, and change is possible.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".