North Atlantic Fisheries in Change. From Organic Associations to Cybernetic Organizations
Bibliographic record
Abstract
During the 1990s radical changes took place in marine ecosystems, fisheries and fishing communities around the North Atlantic.Social-ecological restructuring involving interactive changes in marine ecosystems, harvest technologies, fisheries science, management practices and goals, fishing households and communities and markets radically transformed fisheries associations.This article draws on insights from multiple sources, including a series of career history and other semi-structured interviews with fishers from Newfoundland and Labrador and Norway.These insights are presented in the form of career histories of two fishers, one from North Norway and one from Labrador on Canada's east coast.These career histories are contextualized within the larger literature on the post-World War II history of these two regions and the resulting descriptions are used to inform the design of three ideal types of fishery associations (organic, mechanical and cybernetic) that capture three main phases of interactive socialecological restructuring during this period.Our argument is that today's North Atlantic harvesters are increasingly embedded in cybernetic fisheries organizations that are radically different from the forms of association that dominated in the past.In our analysis and conclusion we highlight the sustainability challenges and opportunities this process of cyborgization poses for these fishers and for North Atlantic fisheries in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".