Brewing with Distinction: The Implications of a Quality Symbol for the Craft Beer Industry of British Columbia
Bibliographic record
Abstract
A growing segment of the Canadian beer market is the microbrew/craft beer segment. For the palates of many beer connoisseurs, craft beer has a taste that distinguishes it from other beers, thus setting it apart from the mass-marketed products of the giant commercial breweries. However, a large proportion of beer consumers in Canada remain oblivious to the virtues and properties that make craft beer unique. This study examines the feasibility of creating a quality symbol of distinction as a means of raising consumer awareness about craft beer, with a particular focus placed on the craft brewing industry of British Columbia. Evidence of the positive impact that a quality symbol has had on the wine industry in British Columbia has prompted initial research on a quality label for the craft brewers of this province. Seventy-two beer consumers were surveyed, at the Canada Cup of Beer festival and the Simon Fraser University downtown campus in Vancouver, BC, to examine the impact that a quality symbol of distinction might have in raising consumer awareness about the craft brewing industry in BC. The results from this study show that the creation of such a symbol of distinction may lead to an increase in consumer awareness about craft beer in BC. The two factors found to be most important, in determining whether a craft beer would receive a quality symbol, were the overall quality of the beer ingredients (e.g., barley, hops, and yeast) and the source of the brewing supply of water. Additional research among the members of the Craft Brewers Association of BC and the provincial liquor control board (BCLDB) is iv warranted, in order to further examine the feasibility of creating a quality symbol of distinction and setting provincial standards of quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".