An analysis of rural watershed adaptation dynamics: Building social-ecological resilience to climate change on the Canadian Prairies
Bibliographic record
Abstract
This thesis is an investigation of how and why rural communities on the Canadian Prairies adapt to environmental change driven by extreme weather events and contributes to the theoretical, empirical, and practical understanding of the process of adaptation. The Prairie climate is distinguished by high variability. Climate change projections suggest warmer temperatures, drier conditions, greater incidence and severity of both drought and extreme precipitation events, and reductions in water availability and quality will prevail in future years. Rural communities will need to adapt to such changes. The process of adaptation, however, is not well studied. Resilience, because of its explicit focus on processes of change in social-ecological systems is thought to have much potential to contribute to climate change adaptation research. This thesis is based on the premise that one of the most promising ways to inform climate change adaptation theory and policy is to study past processes of adaptation to environmental changes believed to be representative of projected future conditions. Using an historical analogue approach the thesis studied the twenty-year soil and water conservation experience of the Deerwood Soil and Water Management Association in the South Tobacco Creek watershed, in south-central Manitoba, building a network of small dams to control runoff and prevent soil erosion, as a successful process of adaptation to the impacts of extreme weather events. The thesis developed a conceptual model of adaptation and an associated analytical framework for practical adaptation assessment that explicitly integrates the concepts of vulnerability and resilience into a model of adaptation as a dynamic and emergent process of learning from exposure experiences to reduce vulnerability by building social-ecological resilience. The conceptual model is grounded in Holling's Adaptive Cycle metaphor and integrates concepts of transformation to adaptive governance and building resilience through increasing the ability to absorb change, self-organize, and innovate, experiment and learn. The analytical framework developed to apply the conceptual model is composed of nine (9) steps, each characterized by a guiding question for analysis. The utility of the model/framework to describe and explain the process of adaptation to environmental change is demonstrated through the case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".