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Record W4362587986 · doi:10.1177/1354067x231169287

Understanding Chinese international students’ perception of cultural conflicts in Canada: Through the case of cannabis use

2023· article· en· W4362587986 on OpenAlexaffabout
Kedi Zhao, Trish Lenz, Lin Fang

Bibliographic record

VenueCulture & Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAcculturationCannabisContext (archaeology)ChinaConceptual frameworkSocial psychologyPsychologySociologyPolitical scienceImmigrationSocial scienceLaw

Abstract

fetched live from OpenAlex

The legalization of recreational cannabis consumption in Canada created a cultural conflict for international students from China, where the use of cannabis is heavily criminalized and deemed immoral. This conceptual paper theorizes this cultural conflict experienced by Chinese international students in Canada by applying three theories from macro to micro levels. Neoliberalism is first used to understand how this cultural conflict exposes collisions between the neoliberal West and the rising economic power of China as illustrated through Chinese students studying in Canada. Next, acculturation theory focuses on these students’ cultural transition and provides further insight into potential strategies for their handling of specific cultural conflicts such as cannabis use. Lastly, Cloninger’s theory of substance use is adopted to explore Chinese international students’ individual reasoning about cannabis use, particularly how they make decisions based on evaluations of various conditions. Building upon the above analyses, an integrated conceptual model is further formed to help us understand Chinese students’ potential perception of cannabis use in Canada. This conceptual framework provides an important theoretical and conceptual base for future research and practice, from which to further explore cannabis use in the context of cultural transition of different immigrant and migrant groups.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.869

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)0.0000.000

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 head0.142
GPT teacher head0.412
Teacher spread0.270 · 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 designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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