Mismatches in salmon social-ecological systems : insights from Canada’s North Pacific Coast
Bibliographic record
Abstract
This dissertation provides an important contribution to the study of institutional fit by providing an empirical examination of mismatches, and solutions for overcoming them, in some of the world’s most resilient and enduring social-ecological systems. Mismatches between institutions and social-ecological systems, often referred to as institutional mismatches or problems of “fit”, are a major sustainability challenge in natural resource management. While mismatches are hypothesized to lead to the degradation of social-ecological systems, mismatches are often explored in a theoretical context rather than through applied case studies that focus on the identification, characterization, causes, and consequences of mismatch for social-ecological systems. This dissertation uses the theoretical concept of “fit” to examine institutional alignment with Pacific salmon social-ecological systems in north and central British Columbia, Canada. Using an empirical case study of the Skeena River watershed, I identify the key attributes and characteristics that are giving rise to problems of fit and their consequences for fundamentally interlinked salmon social-ecological systems, including impacts on Indigenous Peoples’ rights, livelihoods, and approaches to resource management and stewardship. I then explore solutions for alleviating mismatches and improving social-ecological alignment in two other salmon-bearing regions on British Columbia’s north and central coast – the Nass River watershed and the Central Coast. These two examples illustrate how collaborative efforts to characterize the spatial, temporal, and functional characteristics and dynamics of salmon ecosystems can lay a foundation for overcoming mismatches. While my findings are focused on salmon-bearing watersheds in north and central British Columbia, they are generalizable to other social-ecological systems in which mismatches between social and ecological processes and institutions exist and where solutions to mismatches are required in order to ensure the long-term survival and resilience of the social-ecological system.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".