Institutions contributing to system adaptability : the case of flood management in the Fraser Valley
Bibliographic record
Abstract
The flood threat has existed as long as humans have inhabited the Fraser Basin but the context is changing. Climate change is expected to impact streamflow and flood patterns in yet unpredictable ways, at the same time that population, infrastructure and economic activity continue to increase in floodplain areas in the Basin. This challenge is emerging just as significant shifts in relationships between First Nations and non-First Nations institutions in Canada are taking place. All levels of government jointly affect the adaptive capacity of the linked social-ecological system they inhabit together. In the face of such complexity and uncertainty, a system needs to have the capacity to anticipate, learn, adapt and transform, and not just react, in order to persist. The central research question explored in this study is: How does institutional capacity enhance and/or hinder the current, and ongoing, adaptability of the flood management regime? Drawing on the fields of social-ecological systems, disaster management, and organizational resilience, an adaptability lens is combined with Healey et al.'s Institutional Capacity framework (1999, 2003) to explore these questions focusing on the case of a flood management regime involving the City of Chilliwack and Stó:lō Nation communities in the Fraser Valley, British Columbia. The study is based on documentation, direct observation and twelve expert interviews conducted with representatives of key organizations. Sources of Institutional Capacity that enhance adaptability include the presence of divergence and diversity across the system, along with “learning systems” and collective “sensemaking” repertoires (i.e. the ability to interpret and act in novel situations). Barriers to enhancing adaptability were also identified. For example, an overriding belief in structurally-driven flood management is at odds with the nature of the flood hazard and potential changes. As well, the relative proficiency of the emergency management system may undermine longer-term cycles essential for resilience. Overall, the analysis suggests that the flood management regime was adaptable in the short-term. In the mid- to long-term there are important components of institutional capacity that enhance the potential for adaptability, but a number of weak or missing elements threaten to undermine system adaptability if left unaddressed.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".