Fruit forward?: Wine Regions as Geographies of Innovation in Australia and Canada
Bibliographic record
Abstract
Since the turn of the century, a global boom in wine drinking has revitalised many rural regions through consumer interest in wine products. “Wine regions” are often tourism destinations where working vineyards and wineries co-exist with visitor facilities. This chapter examines how two settler society or New World wine business clusters within wider agricultural districts dating from the nineteenth century have achieved economic sustainability through tourism development. The responsiveness by successive generations of Hunter Valley winegrowers to shocks and opportunities has made this Australia’s oldest continually producing wine region. This longevity is the basis for preserving the scenic vineyard landscape required to maintain gastronomic tourism as the more viable source of wine-region income than selling wine. We compare this community’s innovations with those in the Okanagan Valley, one of Canada’s most highly visited wine and culinary tourism regions, with a growing focus on ecological responsibility. These case studies broaden understanding of economic communities in “wine regions” as rural agents adaptive to agricultural tourism sector dependence on metropolitan consumers.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".