Ontario's micro-brewing industry: an exploratory study
Bibliographic record
Abstract
This study researched Ontario’s micro-brewing industry and used the firm’s strategy obtained from policies, plans, and priorities to develop an industry framework to measure the industry’s contribution to Ontario’s standard of living and, ultimately, to Ontario’s economic well-being. Nine microbreweries were surveyed and 14 websites were analysed based on four research objectives: 1) identify industry success factors; 2) validate a portion of Menna’s model; 3) apply lessons of this study to the creation of successful models in new venture creation; 4) instil a uniquely micro-brewing model for affecting growth and benefits in the province of Ontario. Results show that Ontario’s microbreweries thrive in niche markets with unique strategies and distinctive advantages that contribute to Ontario’s economy. This is a first step in understanding key factors that may help Ontario transition to a community-based, creative-oriented enterprise system of wealth creation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".