Recreational marijuana: Ethical positions and consumption status in explaining attitudes, perceived law ethicalness, and perceived corporate social responsibility
Bibliographic record
Abstract
Abstract Drawing from the Ethics Position theory and the Theory of Planned Behavior, this study was conducted with a comprehensive four‐part goal. It seeks to explore the moderating influences of the consumption status and ethical positions in elucidating (i) individuals' attitude toward recreational marijuana consumption, (ii) individuals' perceived ethical stance regarding recreational marijuana legalization, (iii) individuals' CSR perception of firms operating within the marijuana industry often deemed as morally contentious, and (iv) the interrelationships among the aforementioned variables. Based on a sample of 411 Canadians and two grouping variables, ANOVA tests and a structural Equation Modeling were used to test the research hypotheses. The results show that individuals' attitudes toward the consumption of marijuana, perceived ethicalness of legalizing it, and perceived CSR significantly vary across the three groups of consumption status (i.e., non‐consumer, occasional, and regular) and the four groups of ethical positions (i.e., absolutism, exceptionism, situationism, and subjectivism). The SEM results, however, show that, except for a few cases, there were no notable variations in the model's path coefficients among the identified groups. This study has intriguing implications for academicians, practitioners, politicians, managers, social welfare activists, and other stakeholders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| 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".