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Record W4409154691 · doi:10.1002/jtr.70014

Approaching Cannabis Tourism With Shared Value in Legal Contexts

2025· article· en· W4409154691 on OpenAlexafffundabout
Susan Dupej, Hwansuk Chris Choi

Bibliographic record

VenueInternational Journal of Tourism Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsSocial Sciences and Humanities Research CouncilUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTourismCannabisValue (mathematics)BusinessMarketingPsychologyPolitical scienceLawComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Global initiatives in the legalization of recreational cannabis pave the way for a novel category of special interest tourism (SIT) disconnected from illegal drugs and deviance. Changing social attitudes call for a non‐stigmatizing framework for understanding cannabis tourism. This study introduces the concept of shared value from Corporate Social Responsibility (CSR) into cannabis tourism research. This innovative approach is supported by evidence from a qualitative study that explores best practices among cannabis tourism operators in Canada. The analysis reveals how the social values of safe recreational consumption, ensuring governmental accountability, and establishing legitimacy through collective advocacy emerge due to reimagining cannabis products, revaluating productivity, and fostering a supportive industry ecosystem, respectively. A challenge for cannabis tourism suppliers is the lack of regulations for responsible consumption in public spaces. A tourism perspective of legalization highlights a regulatory gap around cannabis consumption, which is an area in need of policy development.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.421
Teacher spread0.370 · 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
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

Citations2
Published2025
Admission routes3
Has abstractyes

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