University Impact on the Development of Industries in Peripheral Regions: Knowledge Organization and the British Columbia Wine Industry
Bibliographic record
Abstract
When universities consider their impact on societies and economies, they typically stress medicine and the so-called STEM subjects: science, technology, engineering, and mathematics. In contrast, this paper considers a perspective from social science, specifically: how can social science overcome barriers to impacting the development of industries in peripheral regions? Our response is based on the importance of knowledge and voice in economic development, and focuses on the distinctive role of public universities in organizing knowledge. It centres on people gaining knowledge and understanding through open-ended inquiry, and experience as learning, guided by pursuit of the spirit of the truth. It stresses knowledge as key to empowering publics to find their voice, express their interests, and influence the strategic development of an industry. The analysis also embraces the idea that ‘art’ can contribute significantly to economic development. Visual images, literary works, installations and the like can provide a creative atmosphere that enhances education. They can enable people to discover meanings, deepen their understanding, foster their imagination, and in turn influence their actions and socio-economic activities - for example, what they choose to do, and how. To illustrate and deepen the analysis, the paper reflects on a partnership between the Okanagan campus of the University of British Columbia, and KEDGE Business School (Bordeaux, France) to support British Columbia to emerge as a globally recognized wine region.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".