What’s the story? Using news articles to examine resilience pathways and domains in the southern New England American lobster ( Homarus americanus ) fishery
Bibliographic record
Abstract
Understanding the resilience of fisheries systems is integral to enabling them to adjust to current and future environmental change. The American lobster (Homarus americanus) fishery in southern New England has experienced widespread declines in abundance since the late 1990s, with significant impacts on the people and communities reliant on this resource. Through an analysis of news articles from 1999 to 2021, we examined 76 lobstermen’s responses to these lobster population declines using a cope, adapt, transform typology and identified factors that affected their ability to respond and their broader resilience. Results from across southern New England show that lobstermen responded in a diversity of ways. These included staying in lobstering full time, diversifying fishing portfolios, diversifying income by taking on part-time non-fishing roles, and in many cases, exiting the lobster fishery completely to pursue other fisheries or alternative jobs. A range of factors were revealed to influence responses, including financial pressures, access to assets such as savings or community infrastructure, occupational attachment, emotional deliberations, regulatory restrictions, and individual or collective actions. Our results reveal the heterogeneous ways in which individual resilience is exhibited, which requires resilience planning to account for people’s diversity of behavior and actions in response to environmental change. The range of influential factors at multiple scales highlights the need for measures that act at different levels of the fishery system to support resilience. Together, these results encourage the need for more integrated, multi-scale approaches to understanding and managing resilience in fisheries systems facing uncertainty and environmental change.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".