A reflective process to explore identity in an emerging wine territory: the example of British Columbia
Bibliographic record
Abstract
Purpose Identity is often used in wine territory narratives but its meaning is rarely explored with industry actors. This paper aims to present the development and application of a four-step iterative process for engaging an industry in a complex and deep reflection about its shared identity: understanding identity; identifying commonalities and differences; developing a shared narrative and sharing best practice. Design/methodology/approach The authors have engaged with over 50 wineries between 2016 and 2018 on the identity of the British Columbia wine territory through workshops, interviews and other conversations. Complementary methods include documentary review and observations. Findings The work shows the applicability of the four-step process. Success depends on building relationships with and across the industry; creating independent, safe learning environments and facilitation by an independent party; allowing for feedback between the steps, continuous reflection and reiteration of steps and making the time for complexity. Practical implications The application of the process in British Columbia shows that success depends on building relationships with and across the industry; creating independent, safe learning environments and making the time for complexity. Originality/value The paper presents the application of a unique process for industry to explore the identity of a wine territory. It focuses on British Columbia, about which little has been written. Through the process, the industry can better understand identity, what it is, why it matters and how it impacts businesses. The paper’s insights can inspire researchers and industries in their thinking and practice about identity.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".