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Record W4310600145 · doi:10.1089/can.2022.0068

Cannabis Advertising Policies in the United States: State-Level Variation and Comparison with Canada

2022· review· en· W4310600145 on OpenAlexaboutno aff
Natasha C. Allard, Jessica S. Kruger, Daniel J. Kruger

Bibliographic record

VenueCannabis and Cannabinoid Research · 2022
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisAdvertisingRecreationConsistency (knowledge bases)State (computer science)Public healthMedical cannabisBusinessPolitical sciencePsychologyMedicineLawPsychiatry

Abstract

fetched live from OpenAlex

Objective: To assess the regulations on Cannabis advertising across U.S. states for variation and compare with Canadian federal policies, for the purpose of identifying opportunities to protect the public, especially the youth and other vulnerable populations, from health risks. Methods: We reviewed Health Canada's Cannabis Act and Cannabis Regulations to identify prohibited marketing and advertising activities for cannabis products. The Canadian guidelines (where cannabis is legal for both medical and nonmedical use) were compared with regulations in the 36 U.S. states where cannabis is legalized for medical and/or adult (e.g., recreational) use. Results: Cannabis advertising regulations vary greatly and have little consistency across the U.S. states. Most states do not address many of the cannabis advertising activities that are prohibited in Canada. Among the 31 states that do allow some form of cannabis advertising, 74% explicitly prohibit targeting or appealing to minors and 68% prohibit making false or misleading claims. There are 11 illegal advertising tactics in Canada, such as glamorization and testimonials, that were not specifically discussed in any of the U.S. state policies. Conclusion: The lack of consistent marketing guidelines could expose youth and vulnerable populations to cannabis advertisements; more widespread or federal guidance is needed.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.005
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 head0.098
GPT teacher head0.387
Teacher spread0.289 · 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
GenreReview

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

Citations22
Published2022
Admission routes1
Has abstractyes

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