“Craft beer is good not only if it does good, but if it is good” : how key decision makers in craft breweries across British Columbia (BC) understand doing good
Bibliographic record
Abstract
Extensive research has been conducted by food and tourism theorists on the rise of meaningful consumption, conscious consumers, and ability for products to hold cultural value. Today, there is limited research on how key decision makers in small businesses understand and demonstrate conscious and meaningful production (Barman, 2016). Specifically, how key decision makers, in the craft brewing context understand and demonstrate their morals and values. Craft breweries in British Columbia (BC) are a worthy site for this research, as this industry has experienced outstanding growth and expansion across the province. In this research, I ask how key decision makers in craft breweries across BC construct, defend, and maintain ideas of doing 'good'. My research proposes that respondents use a wide range of strategies to discuss and do good business. In 2021, I conducted and analyzed 31 in-depth, one-on-one, semi-structured interviews with industry participants (Said, 2019) using video-conferencing technologies. Results show that my respondents understand doing good in three main ways. First, respondents share an attitude of comradery and a 'rising-tide lifts all boats’ mentality to fight against obstacles like ‘big-beer’ and the Covid-19 pandemic. Second, respondents believe good craft beer is authentic craft beer. To some, authenticity is understood through facets like process and choice of ingredients. Third, respondents use their tasting rooms as third places (Oldenburg, 1999) to foster community. My results confirm that key decision makers draw from a strong cultural repertoire (Cohen & Dromi, 2018) to communicate moral values and generate positive social action (Swidler, 1986). Respondents participate in various value creation activities (Xie et al., 2008), such as making ‘benefit brews.’ Through these, key decision makers can choose to donate proceeds to a notable cause or support their local arts communities by hosting exhibits in their tasting rooms and printing artists’ work on their beer cans. These results are significant for understanding the scope of meaningful production and how craft breweries across BC can create positive effects on their local communities in economic and social ways. Future research might include conducting semi-structured interviews with key decision makers in macrobreweries to understand how they interpret similar themes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".