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Record W4376603807 · doi:10.1177/09646639231173051

Registering Cosmetics? The Constitution of Legal Form and Injurious Substance in Canada (1945–1946)

2023· article· en· W4376603807 on OpenAlexfundaboutno aff
Lara Tessaro

Bibliographic record

VenueSocial & Legal Studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Kent
KeywordsCosmeticsConstitutionLawLegislationLegislatureDoctrineScholarshipPolitical scienceGovernment (linguistics)ParliamentMateriality (auditing)SociologyPoliticsArtMedicineAesthetics

Abstract

fetched live from OpenAlex

In midcentury Canada, legislative drafters, government lawyers, food and drug officials, and ministers grappled with cosmetics. Faced with constitutional concerns about cosmetic licensing, these actors drafted legislative amendments that would instead require cosmetics to be registered. In contrast to people or land, the registration of products, substances, or things has received little attention in sociolegal scholarship. Building on work investigating law's temporalities and materiality, this account traces how in-formed by the constitutional doctrine that apprehended substances through the legal form of prohibition, cosmetics were rendered in draft legislation as constituted of ingredients that may cause injury. Injury, in this account, is a material-temporal regime. Yet cosmetic injury was neither static nor singular, as it was catalysed differently by distinctive regulatory devices. This is shown by last-minute changes to the bill which retooled cosmetic registration, from an information extraction device for anticipating future harms, into a recording device for capturing latent harms.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.846

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0240.022
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.046
GPT teacher head0.254
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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