The cannabis festival: quality, satisfaction, and intention to return
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how festival quality, satisfaction and intention to return among cannabis festival attendees were interrelated by using the 2018 Mile High 420 Cannabis Festival in Denver, Colorado, USA. Design/methodology/approach This study employed an online survey with festival attendees to the 2018 Mile High 420 Festival. A total of 664 attendees participated in the survey. Findings Findings of the study revealed the demographic profile of cannabis festival attendees (i.e. relatively young, single and evenly distributed in terms of gender and residency) and its relationships with respondents’ perceived festival qualities. In addition, two dimensions of festival quality unique to the context of marijuana festival influenced attendees’ satisfaction and intent to return significantly. Festival attendees’ travel characteristics were used to describe attendees’ satisfaction and intent to return to a different degree. This research has also highlighted a lack of research in the area of cannabis events/festivals. Originality/value This study is the first investigation that studied a cannabis-themed festival in the tourism literature. As legalization of recreational cannabis has been embraced in the USA and abroad (i.e. Canada), the findings of this empirical study will help the industry professionals and policy makers to understand this unprecedented SIT market and can be used as the benchmarks for their legal and operational practicality. Further, this study highlights research gaps in the tourism literature, and identifies those areas where future study is unlikely to provide new knowledge.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".