Non-Economic Impact of Craft Brewery Visitors In British Columbia: A Quantitative Analysis
Bibliographic record
Abstract
The number of craft breweries in British Columbia has grown significantly in recent years, numbering over 140 in 2017. Very little is known about the effects of the craft brewery industry in British Columbia, specifically as it relates to impacts not related to brewery revenue and job creation. Beyond British Columbia, the craft beer industry has not empirically examined nonrevenue impacts in a manner that reflects the global growth of the sector. Tourism experiences, such as those offered by craft breweries, are becoming increasingly important for resilience and sustainable growth and success of destinations. The goal of this research was to determine who visitors to craft breweries are, how tourist and resident patrons differ, and what effects craft breweries have on tourists who visit breweries. A 55-item survey was distributed at 11 craft breweries in three regions in British Columbia during the summer of 2017. Results found differences between tourist and resident patrons in selfimage congruency, age, and travel party size, but no difference in gender, education, or household income. From a tourism standpoint, it was found that memories have a significant, positive impact on loyalty regarding the brewery and the destination. For tourists, strong connections were found between social involvement and both authenticity and place attachment for those who were more socially involved in craft beer. Comparisons to previous research in the wine industry provide additional commentary. Implications for craft breweries, destinations, and future research in this area are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".