Case Study on the Impact of Cannabis Legalization: Understanding through Canada & the Netherlands
Bibliographic record
Abstract
This paper elaborates on the complex legalization of marijuana and the impacts on the tourism and hospitality sectors of Canada and the Netherlands. The piece will look at the transition from prohibition to legalization and implications for the development of cannabis-related tourism experiences. In Canada, legalization has fostered numerous kinds of cannabis-related tourism experiences, such as guided tours of producers and visiting retail outlets, which have vigorously impacted market growth and public sentiment. The decriminalization policy and the unique coffee shop culture in the Netherlands have been significantly responsible for shaping the tourist pattern, making it one of the most important models of cannabis tourism. \nThis thesis, aims to examine the impact of cannabis legalization by understanding the case countries through the difference in their path to legalization, understanding the effects of cannabis legalization on society and how it changes the behaviour patterns of the tourists. This research provides insights to hospitality and tourism Industry specialists, business owners, governing bodies. \nThe paper focuses on qualitative research, making it into a case study by critically assessing the figures and patterns of tourist behavior in consumption for a thorough analysis of the economic, social, and regulatory impacts of legalization. The data has been picked from BMJopen, Statista, Prohibition partners etc. The results showed that both countries have benefited greatly in terms of revenue from tourism driven by increased demand in cannabis-related experiences and in search of novelty and authenticity. There remain, however, challenges, more so in robust regulatory frameworks that are going to be put in place to mainstream cannabis tourism in the mainstream market. \nFurther, the thesis also makes a number of regulatory recommendations aimed at allowing the benefits accrued from cannabis tourism and minimizing the various risks associated with it. This includes stringent licensing requirements, zoning laws that are meant to regulate where and the density of cannabis businesses, and even public health campaigns. In this regard, it is helpful to policy makers, industry players, researchers, and significant in contributing to the discourse on whether the legalization of marijuana is beneficial or bad for the tourism and hospitality industries.
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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 | 0.004 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".