The unmaking of the Skeena River salmon fisheries as a social-ecological system
Bibliographic record
Abstract
Commercial salmon fisheries on the Skeena River in northern British Columbia have been a way of life, a vital part of the economy, and a valued support to community health and wellbeing in the region for over a century.In the last two decades a drastic curtailment of fishing opportunity has reduced commercial landings and fishing effort to less than 20% of where they stood in the mid-1990s and earlier.Ostensibly undertaken in the interests of conservation, the reduction in commercial access to salmon stocks is a much more complex story.This dissertation poses the question: what, if anything, would make commercial salmon fisheries on the Skeena "sustainable"?Starting from the premise that sustainability in fisheries is about more than the resource that is being harvested, I present a fishery-focused social-ecological system model that includes markets, communities, ecosystems and governance institutions.I situate the Skeena salmon fisheries in this model as a first step.I then turn to the management system to see how it addresses the issue of sustainability.Using a framework that was developed through the Canadian Fisheries Research Network (CFRN), I evaluate management on the Skeena over the past 30-40 years in three dimensions: ecological, socio-economic, and governance.Having shown that sustainability on the Skeena continues to be narrowly defined in terms of the productivity of salmon populations, I introduce a second model to represent how natural resources are meant to be exploited under conditions characteristic of "modernity".I call this a "utilitarian control system" model: it shows how fisheries managers on the Skeena have been compelled to severely restrict the type and quantity of value extracted from the fishery in order to maintain an illusion of control over the resource production system.I conclude by presenting an alternative approach to sustainability that I term natural governance.Consisting of three primary systemsnatural, governance and socialwith three corresponding functionsdiversity, legitimacy, and wellbeing -I apply the framework to the Skeena fisheries as a way of generating recommendations for how to begin the transition to a healthier relationship between human and natural systems.
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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 | 0.002 | 0.002 |
| 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 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".