From Policy Instruments to Action Arenas: Toward Robust Fisheries and Adaptive Fishing Households in Southwest Nova Scotia
Bibliographic record
Abstract
abstract: The coastal fishing community of Barrington, Southwest Nova Scotia (SWNS), has depended on the resilience of ocean ecosystems and resource-based economic activities for centuries. But while many coastal fisheries have developed unique ways to govern their resources, global environmental and economic change presents new challenges. In this study, I examine the multi-species fishery of Barrington. My objective was to understand what makes the fishery and its governance system robust to economic and ecological change, what makes fishing households vulnerable, and how household vulnerability and system level robustness interact. I addressed these these questions by focusing on action arenas, their contexts, interactions and outcomes. I used a combination of case comparisons, ethnography, surveys, quantitative and qualitative analysis to understand what influences action arenas in Barrington, Southwest Nova Scotia (SWNS). I found that robustness of the fishery at the system level depended on the strength of feedback between the operational level, where resource users interact with the resource, and the collective-choice level, where agents develop rules to influence fishing behavior. Weak feedback in Barrington has precipitated governance mismatches. At the household level, accounts from harvesters, buyers and experts suggested that decision-making arenas lacked procedural justice. Households preferred individual strategies to acquire access to and exploit fisheries resources. But the transferability of quota and licenses has created divisions between haves and have-nots. Those who have lost their traditional access to other species, such as cod, halibut, and haddock, have become highly dependent on lobster. Based on regressions and multi-criteria decision analysis, I found that new entrants in the lobster fishery needed to maintain high effort and catches to service their debts. But harvesters who did not enter the race for higher catches were most sensitive to low demand and low prices for lobster. This study demonstrates the importance of combining multiple methods and theoretical approaches to avoid tunnel vision in fisheries policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".