Bibliographic record
Abstract
Building on a rich body of feminist scholarship on estrogen, this account interrogates how potent estrogenic cosmetics and consumer product labels emerged together, through the regulatory practices of scientists and lawyers, in mid-century Canada. Composed from archival and other primary sources, the story traces the development of Canada’s first cosmetic regulations – which applied only to cosmetic products containing estrogens. In 1944, “sex hormones” had been the first substances for which the Department of National Health and Welfare adopted labels in lieu of dose or potency standards under the Food and Drugs Act. With dose-response thresholds thus written out of the Sex Hormone Regulations, in 1949, regulators devised a new type of consumer product label that warned women to use estrogenic cosmetic products “with care”. Further regulatory amendments in 1950 appeared, on their face, to require positive proof of safety for estrogenic cosmetics, However, through varied administrative and enforcement practices that hinged upon “directions for use” in product labels, National Health officials quietly reintroduced dose-response logics back into estrogen regulation. As legal technologies for disciplining women consumers to regulate their own exposures, product labels were becoming instrumental. With labeling, estrogen catalyzed an early example of risk regulation in Canada.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".