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Record W3024845399 · doi:10.28968/cftt.v5i2.32339

Potency and power

2020· article· en· W3024845399 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCatalyst Feminism Theory Technoscience · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
FundersYork University
KeywordsCosmeticsEnforcementProduct (mathematics)ScholarshipEstrogenBusinessPolitical sciencePublic relationsMedicineLawInternal medicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.239
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it