Knowledge On Tap: Measuring Sustainability Impacts of Ontario Craft Brewers
Bibliographic record
Abstract
Small businesses compose 98% of all employer businesses and employ nearly two-thirds of the entire labour force in the Canadian economy. Small businesses, however, are often exempt from environmental regulation and corporate social responsibility mandates. As a result, small business impacts on host community economic, social, and environmental factors are often not adequately documented. The craft beer sector offers a suitable environment for further exploration: these businesses are small by definition, numerous, and their production methods are resource-intensive and inefficient. This research assembles a large panel dataset and causally explores how the presence of craft breweries impact the economic, social, and environmental performance of their host localities in Ontario. Findings indicate that the presence of a brewery in an Ontario community results in mixed sustainability outcomes: reductions in unemployment rates, nitrogen dioxide emissions, and PM2.5 emissions and increases in household income; while increasing sulfur dioxide emissions and decreasing per-capita populations of visible minorities and indigenous people. The analysis also shows that the results' magnitude and direction of effect varied depending on whether the brewery was located in an urban or rural area. This thesis presents a causal impact analysis for a growing small business segment at a provincial scale.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".