Spawning trouble: A criminological examination of salmon aquaculture in coastal British Columbia
Bibliographic record
Abstract
Despite considerable evidence of ecological harm, ongoing breaches of law and regulation and systemic failure on the part of regulators, the salmon aquaculture industry has to date been spared criminological consideration.This dissertation aims to begin to address this lacuna through an interrogation of the discourse of environmental harm and risk associated with salmon farming in British Columbia, as represented through a significant moment in its history, the Commission of Inquiry into the Decline of Sockeye Salmon in the Fraser River.An ethnographic content analysis of the Commission hearings was undertaken, which drew on the Framework methodology.In making meaning of the data, I enlist several theoretical frameworks, including political ecology and risk as theorized in risk society and governmentality scholarship.To this end, I draw on the work of Ulrich Beck, for whom the development of the "risk society" in which a critical-reflexive engagement with the ecological risks of techno-industrialization is a central preoccupation.This is compared with analyses derived from Michel Foucault, where risk is viewed as a form of governmentality.I contend that the environmental governance of salmon aquaculture through "sustainable development" manifests an expression of biopolitical power, deriving from and operating upon a network of relations between the population, the resources upon which it depends and the environment.Material relations are also considered through the lens of Treadmill of Production theory, with a focus on both the drivers of the treadmill as originally conceptualizedcapital, labour and stateand countervailing forces such environmental and Indigenous groups.Through a process of capital accumulation via intensive agri-industrial production, the salmon aquaculture industry externalizes the costs of its ecological additions and withdrawals, engendering local, regional and even global impacts through spatially and temporally networked global systems of production and consumption.In this dynamic, the regulatory system is a site of contestation.I consider this adumbration of the material and ideological relations of power with a generative intent and take up some of its overarching implications for engaging with the regulation of salmon aquaculture and with other systems of ecological governance in British Columbia and beyond.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.021 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".