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Tourism As an Agent of Cannabis Normalization: Perspectives from Canada

2021· article· en· W3217315415 on OpenAlexaffabout
Susan Dupej, Sanjay K. Nepal

Bibliographic record

VenueTourism Review International · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of WaterlooUniversity of Guelph
Fundersnot available
KeywordsLegalizationCannabisTourismNormalization (sociology)RecreationBusinessMarketingPolitical scienceSociologyPsychologyLawSocial science

Abstract

fetched live from OpenAlex

The 2018 legalization of cannabis in Canada provides an opportunity within a federally legalized context to offer recreational and leisure experiences that incorporate the purchase, consumption, production, and education of cannabis. The establishment of cannabis tourism as a tolerated and increasingly widespread and socially significant practice under the frameworks of legalization and normalization challenges its association with deviance in the tourism literature. The purpose of this article to rethink cannabis tourism as an agent of normalization. In adopting cannabis as a resource, the tourism industry sets standards that become embedded in a broader context of social acceptance. Evidence from a study that documents cannabis tourism in Canada in the first few years following legalization is used to illustrate how tourism suppliers have adopted cannabis as a resource. This article contributes a qualitative assessment of normalization to the literature through an examination of both a database of cannabis tourism-related businesses and the narratives of suppliers in the cannabis tourism industry. Tourism is conceptualized as an agent of normalization by illustrating how it facilitates the accessibility and availability, everyday prevalence, increased tolerance, and social and cultural accommodation of cannabis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.021
GPT teacher head0.339
Teacher spread0.318 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

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