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Record W3139266291

The Limits of Local Authority Over Recreational Cannabis

2019· article· en· W3139266291 on OpenAlexaffabout
Felix Hoehn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCannabisLegalizationLegislationRecreationStatutory lawBusinessLocal governmentScope (computer science)Local authorityLocal communityEffects of cannabisPublic administrationPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

With the legalization of recreational uses of cannabis Canadian local governments will need to regulate some cannabis-related activities. Thanks to statutory enhancements of the powers of municipalities and greater judicial respect for municipal authority, local governments generally have flexible powers that are broad enough to allow them to regulate the location and other aspects of cannabis-related activities, including consumption in public places and licensing cannabis-related businesses. Local governments can also ban some or all cannabis-related activity, either by taking advantage of local option clauses included in some provincial and territorial legislation, or by grounding bans on purely local powers. Local bans of activities such as home cultivation, cannabis lounges or retail sales will be valid if they are narrow in scope, enacted for a municipal purpose, and do not frustrate the purpose of the Cannabis Act. Broader local bans will face greater challenges, and would need to be geographically limited to minimize the impact on national objectives. They would also need to be grounded in a municipal purpose such as health and safety or protecting a unique cultural or religious community character.

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)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.239
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.020
Scholarly communication0.0090.005
Open science0.0020.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2019
Admission routes2
Has abstractyes

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