www.elsevier.com/locate/gloenvcha Multiple exposures and dynamic vulnerability: Evidence from the grape industry in the Okanagan Valley, Canada
Bibliographic record
Abstract
This paper assesses the vulnerability of grape growers and winery operators in the Okanagan Valley, British Columbia to climate variability and change, in the context of other sources of risk. Through interviews and focus groups, producers identified the climatic and non-climatic risks relevant to them and the strategies employed to manage these risks. The results show that the presence of multiple exposures affects the way in which producers are vulnerable to climate change. Producers are vulnerable to conditions that not only affect crop yield, but also affect their ability to compete in or sell to the market. Their sensitivity to these conditions is influenced in part by institutional factors such as trade liberalization and a ‘‘markup-free delivery’ ’ policy. Producers ’ ability to adapt or cope with these risks varies depending on such factors as the availability of resources and technology, and access to government programmes. Producers will likely face challenges associated with the supply of water for irrigation due to a combination of climatic changes and changing demographics in the Okanagan Valley, which in turn affect their ability to adapt to climatic conditions. Finally, adaptations made by producers can change the nature of the operation and its vulnerability, demonstrating the dynamic nature of vulnerability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".