Ocean frontier assemblages: Critical insights from Canada's industrial salmon sector
Bibliographic record
Abstract
Abstract The ocean frontier has become central to a range of new and emerging strategies aimed at realizing the potential of the ocean economy. The purpose of this paper is to critically examine the configuration of the ocean as a frontier and its role in transforming marine spaces through the case of salmon aquaculture in Canada. To this end, we engage with ‘frontier assemblage’, an analytic that is developed from scholarship on agrarian and extractive resource frontiers in Asia. We use this approach to identify and extend three interrelated conceptual sensibilities. First, we use ‘frontierization’ to suggest that ocean frontier spaces are not only articulated at leading edges. Instead, frontierization happens at indeterminate sites, including those that have undergone earlier rounds of capitalist resource extraction. Second, we explore how ocean frontier resource extraction is assembled in ways that are indeterminate, but not radically open. Using the case of salmon aquaculture in Newfoundland, we show how resource extraction could have been ‘otherwise’. Third, we critically assess the promissory politics that are key to the ocean frontier. We argue that the frontier assemblage analytic—and the sensibilities we use—provides an approach to critically assess strategies aimed at realizing the ‘untapped’ resources of the ocean frontier.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.026 | 0.020 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".