Bibliographic record
Abstract
Although there is considerable historical literature describing the social and economic impact of drought on the prairies in the 1930s, little has been written about the challenges presented by drought in more contemporary times. The drought of 2001-02 was, for example, the most recent large-area, intense, and prolonged drought in Canada and one of Canada's most costly natural disasters in a century. Vulnerability and Adaptation to Drought describes the impacts of droughts and the adaptations made in prairie agriculture over recent decades. These adaptations have enhanced the capacity of rural communities to withstand drought. However, despite the high levels of technical adaptation that have occurred, and the existing human capital and vibrant social and information networks, agricultural producers in the prairie region remain vulnerable to severe droughts that last more than a couple of years. Research findings and projections suggest that droughts could become more frequent, more severe, and of longer duration in the region over the course of the 21st century. This book provides insights into the conditions generating these challenges and the measures required to reduce vulnerability of prairie communities to them. Developing greater understanding of the social forces and conditions that have contributed to enhanced resilience, as well as those which detract from successful adaptation, is a principal theme of the book. To that end, the book examines drought through an interdisciplinary lens encompassing climate science and the social sciences. Two of the chapters are based on the drought experiences of other countries in order to provide a comparative assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.888 | 0.886 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".