Recreational Marijuana Legalization Related Concerns and Perception in South Asian Immigrant Communities
Bibliographic record
Abstract
South Asian immigrants comprise approximately 25% of Canada’s immigrant population, however, due to minimal efforts taken to initiate consultation on the preparedness and concerns regarding marijuana legalization within the South Asian community, the current knowledge gap has facilitated the acquisition and spread of misinformation in respect to marijuana use. Levels of unawareness were investigated through the use of a 25 question structured survey with two optional written response sections, and a total of 300 surveys were distributed in the first phase of the research investigation. Participant eligibility was assessed on the basis of (1) being over the age of 18, and (2) first generation immigrant and of South Asian ethnicity. 57% of respondents were males, and 43% were female. 31.5% of participants were between the ages of 36-45, constituting the most common age group for this study. Our survey results indicated that lack of awareness pertaining to the legalization of marijuana was influenced heavily by gender, level of education, and ethnicity. 88.2% of respondents stated that they were unaware of any community led initiatives set in place to educate either youth or adults on the effects of recreational marijuana use, along with the details of the current recreational marijuana legislation. When assessing levels of unawareness, it was found that 23% of males and 35.71% of females were unaware of either the legalization of recreational marijuana or the details within the legislation. It was also found that lower levels of education correlated positively with lower levels of awareness in respect to recreational marijuana legalization, with 36.36% of individuals without a high school certification indicating that they were unaware of the legalization of recreational marijuana and/or the details within the legislation, the highest of any group. Levels of unawareness in regards to either the legalization of recreational marijuana and the details within the legislation were found to be alarmingly high in the South Asian community. Moreover, females and individuals without high-school and post-secondary education were more likely than males and individuals with either bachelor or professional degrees to report unawareness. It was also found that little to no efforts have been taken by community organizations to promote knowledge dissemination and mobilization within the South Asian immigrant population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".