Regulating Toxics: Sex and Gender in Canada's Chemicals Management Plan
Bibliographic record
Abstract
Chemical substances are found everywhere in our environment. Whether it be at home, outdoors, or in the workplace, we are continuously coming into contact with various chemicals through our air, water, food, cosmetics, clothes, personal care products and everyday household items (Cooper, Vanderlinden, and Ursitti 2011; Program on Reproductive Health and the Environment 2008). As our detection methods improve, we are increasingly forced to confront the evidence of these exposures: biomonitoring studies now show that nearly everyone has measurable amounts of almost all known toxic chemicals stored somewhere in their bodies (CDC 2013; Environmental Defence 2009; Statistics Canada 2012). At the same time, we are witnessing a rise in incidence of a number of diseases and disorders in men and women. These include mutagenic illnesses, irreversible developmental and neurodevelopmental syndromes, reproductive disorders, and a number of autoimmune diseases. Many scientists, environmental groups and health practitioners suggest that the rising incidence of many of these disorders and diseases can be tied to chemical exposures in our environment (Cooper, Vanderlinden, and Ursitti 2011).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".