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Record W4283740562 · doi:10.3390/cosmetics9040072

Overview of Cosmetic Regulatory Frameworks around the World

2022· article· en· W4283740562 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.

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

VenueCosmetics · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Packaging Perceptions and Trends
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaUniversidade de Lisboa
KeywordsCosmeticsBusinessEuropean unionChinaCategorizationMarketingWork (physics)Risk analysis (engineering)International tradePolitical scienceEngineeringComputer scienceLawMedicine

Abstract

fetched live from OpenAlex

To ensure safety and efficacy, cosmetic products are regulated and controlled worldwide. However, the regulatory approaches of each country may be significantly different and impact the competitiveness and economic viability of the industry. This work presents an updated review and comparison of regulatory requirements from the European Union, United States of America, Canada, Japan, People’s Republic of China and Brazil. It outlines contents such as the definition, classification and categorization of cosmetics, pre-market requirements, ingredients management, general labelling requirements, regulation of claims concerning advertisement and commercial practices, increase of animal testing and marketing bans on cosmetic products. Furthermore, it weighs the impact of regulatory differences on the safety and accessibility of these products in the mentioned regions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.712
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.279
Teacher spread0.233 · 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