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Record W4366822568 · doi:10.1111/add.16201

Do not let the ideal be the enemy of good enough regulation

2023· letter· en· W4366822568 on OpenAlexaboutno aff
Wayne Hall, Janni Leung, Beatriz H. Carlini

Bibliographic record

VenueAddiction · 2023
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisLegalizationPublic healthBusinessPublic economicsLegislationMarketingAdvertisingMedicinePolitical scienceLawEconomicsPsychiatry

Abstract

fetched live from OpenAlex

Research on the effectiveness and efficiency of different methods of regulating cannabis potency should be a high priority for public health research that will inform the design of cannabis regulations that minimize public health harms. Our article [1] was intended to alert the addictions field to the critical issue of increased cannabis potency as a public health concern, counter the cannabis industry argument that such regulation is unnecessary and canvas some regulatory options. We thank our commentators for their thoughtful responses, which reveal that regulating tetrahydrocannabinol (THC) potency is more complex than it seems at first sight. Freeman & Lorenzetti highlight consumers’ need for simpler advice on labels, much as standard drinks of alcohol [2]. They have led consensus projects to define standard doses of THC that could be used in this way. They also make the useful point that regulators need to consider setting minimum unit prices for cannabis, much as those that have been implemented to reduce heavy alcohol consumption in some countries. Pardal & Wadsworth highlight the fact that the US model of cannabis legalization—a commercialized for-profit market, with minimal regulation of potency and promotion—is not the only model on offer [3]. Uruguay has limited sales to herbal cannabis and capped the THC content of cannabis sold in pharmacies. The Canadian province of Quebec has banned sales of cannabis extracts, limited the sale of edibles and imposed a cap on the THC content of herbal cannabis. The effectiveness of these policies is well worth investigation. The major empirical question is whether the policies will succeed in the longer term, when neighbouring jurisdictions allow the sale of banned products that can be easily transported across borders. Caulkins points out the economic drivers of increasing cannabis potency in the United States; namely, cannabis producers’ need to make maximum use of whole cannabis plants to compete in a market in which cannabis prices are declining [4]. He argues that bans on the sale of high-potency cannabis products would be simpler and easier to implement than attempting to regulate the potency of the many different cannabis products in US legal cannabis markets. He cites evidence that bans need not generate large-scale illicit markets. We agree that banning high-potency products could be the most effective measure to protect public health in countries contemplating cannabis legalization. We doubt its feasibility, however, in mature US markets that already sell high-potency products and in which cannabis retailers will strenuously oppose the policy. Governments regulating these markets may also prefer the revenue from taxing cannabis products on the basis of their THC content. The regulation of cannabis potency is nowhere near as straightforward as regulating alcohol, but we should not allow the cannabis industry to use this as a reason for failing to regulate cannabis potency. In cannabis regulation, as in any area of public health, we should not allow the ideal to be the enemy of the good enough. Research on the effectiveness and efficiency of different methods of regulating cannabis potency should be a high priority for public health research that will inform the design of cannabis regulations which minimize public health harms. Open access publishing facilitated by The University of Queensland, as part of the Wiley - The University of Queensland agreement via the Council of Australian University Librarians. None.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.252
Threshold uncertainty score0.601

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.300
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractyes

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