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Record W74659616 · doi:10.1177/009885881303900202

Commercial Speech Law and Tobacco Marketing: A Comparative Discussion of the United States and Canada

2013· article· en· W74659616 on OpenAlexaboutno aff
Micah L. Berman

Bibliographic record

VenueAmerican Journal of Law & Medicine · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutionCommercial speechLawArgument (complex analysis)Tobacco industryStatement (logic)Political scienceFirst amendmentLegislationConstitutional amendmentTobacco controlSupreme courtMedicinePublic health

Abstract

fetched live from OpenAlex

In November 2011, U.S. District Court Judge Richard Leon ruled that the U.S. Food and Drug Administration's (FDA’s) proposed graphic health warnings for cigarette packages violated tobacco companies’ First Amendment rights. In doing so, he pointedly refused to consider the experiences of Canada, the United Kingdom, and the more than thirty other countries that had adopted similar graphic warnings in the past decade. Rather, he swatted away all references to those other countries’ experiences by stating (first at oral argument and then in his decision) that “none of [those countries] afford First Amendment protections like those found in our Constitution.” While it is true that no other country uses the First Amendment per se, many other countries do offer constitutional protection to freedom of speech and/or freedom of expression. Indeed, several other countries apply “strikingly similar” legal tests when reviewing restrictions on speech (and on commercial speech in particular). Thus, the statement that other countries do not “afford First Amendment protections like those found in our Constitution” is an oversimplification.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.019
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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations5
Published2013
Admission routes1
Has abstractyes

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