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Record W3133595689 · doi:10.3390/ijerph18052472

Disciplinary Approaches for Cannabis Use Policy Violations in Canadian Secondary Schools

2021· article· en· W3133595689 on OpenAlexafffundabout
Megan J. Magier, Scott T. Leatherdale, Terrance J. Wade, Karen A. Patte

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsUniversity of WaterlooBrock University
FundersInstitute of Population and Public HealthInstitute of Nutrition, Metabolism and DiabetesCanadian Institutes of Health ResearchHealth Canada
KeywordsCannabisPermissiveDisciplinePunitive damagesLegalizationMental healthSchool disciplinePsychologyMedicinePsychiatryPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

The objective of this study was to examine the disciplinary approaches being used in secondary schools for student violations of school cannabis policies. Survey data from 134 Canadian secondary schools participating in the Cannabis use, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary behaviour (COMPASS) study were used from the school year immediately following cannabis legalization in Canada (2018/19). Despite all schools reporting always/sometimes using a progressive discipline approach, punitive consequences (suspension, alert police) remain prevalent as first-offence options, with fewer schools indicating supportive responses (counselling, cessation/educational programs). Schools were classified into disciplinary approach styles, with most schools using Authoritarian and Authoritative approaches, followed by Neglectful and Permissive/Supportive styles. Further support for schools boards in implementing progressive discipline and supportive approaches may be of benefit.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.073
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0080.003
Scholarly communication0.0030.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.279
GPT teacher head0.493
Teacher spread0.214 · 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 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
Published2021
Admission routes3
Has abstractyes

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Same venueInternational Journal of Environmental Research and Public HealthSame topicEducation Discipline and InequalityFrench-language works237,207