The nature of human adaptation : exploring local water resource management in the Okanagan region
Bibliographic record
Abstract
Using climate change adaptation theory as a framework, this study explores the process of adaptation to multiple stressors in the context of water management in the Okanagan Region, British Columbia. Water resources in the Okanagan are under growing stress from many pressures, including population growth, irrigated agriculture, tourism activities, forestry at higher elevations and now climate change. How to effectively adapt to these multiple stressors is a pertinent question for both local and provincial decision-makers. Four case studies, each representing different water efficiency approaches were selected for the study: domestic metering in Kelowna, irrigation metering in SEKID, wastewater reclamation in Vernon and institutional change in Greater Vernon, specifically amalgamation of separate water utilities. The primary objective of the study is to explore how local authorities are adapting to current changing circumstances that impact availability of water resources: what factors triggered adaptation, determine the options selected and the success or failure of implementation, as well as what capacities facilitated adaptation i.e. adaptive capacity. Exploration of adaptation from a multi-signal perspective accentuates the contextual nature of future adaptation to climate change; that many factors i.e. other environmental pressures, socio-economic and political issues, will ultimately constrain, impede or encourage effective adaptation. Secondary objectives of the study include analysing the effectiveness of the four management practises and exploring the role of learning in the adaptation process. 28 interviews of local water managers, Council/Board members and other key informants were undertaken. These cases show that adaptation, even planned adaptation, is not a rational, clear-cut process. Five key elements are critical for the initiation and follow through of appropriate and effective adaptation: 1) Capacity; 2) Willingness; 3) Understanding; 4) Trust, and 5) Learning. Resources need to be available/accessible in order for adaptation to occur. Willingness, or human agency, is vital in making appropriate decisions. Understanding the context will aid selection of appropriate options, aid procedural ease and outcome effectiveness. Trust, although won't necessarily prevent conflict will ease the decision-making process. Finally, making learning an explicit objective will challenge internal status quo and ensure continual system improvements as well as the diffusion of experience between organisations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".