A Case Study In Water Sustainability: The Craft Brewing Industry In Alberta and California
Bibliographic record
Abstract
Access to high quality, reliable freshwater resources has been recognized as a global issue for decades and as the demand for water continues to grow, water management and sustainability issues have been pushed into the limelight. Despite this mounting pressure, variation in water use practices continues to persist, which contributes to both local and global water security challenges. Drawing on the environmental management literature, I noticed that the majority of the existing research focuses on the role of managerial demographics rather than on the process of how managers think, interpret, and act in strategic situations. Thus, in an effort to address this gap I adopted a qualitative research approach, conducting semi-structured interviews with managers at craft breweries in Alberta and California. My findings indicate that managerial sensemaking acts as a mediating process in a manager’s choice of water management strategy, ultimately influencing their brewery’s water use performance. And, that these relationships are affected by managerial characteristics as well as contextual factors. As the world’s demand for fresh water, and the number of people living in water stressed conditions continues to rise, these findings have important implications. By both extending and contributing to existing sensemaking and cognitive frame theory, my findings shed light on alternative cognitive determinants driving water use variation and thus support the development of more sustainable water management practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".