Sustaining island fishing communities: Policy and management in practice in Maine and Newfoundland
Bibliographic record
Abstract
This thesis examines the relationship between fisheries policy, fisheries management, and island community development in Maine and Newfoundland. Three research questions and themes guided this work: 1) what kind of relationship there is between island community development, fisheries policy, and fisheries management; 2) how communities responded to changes in the fishery; and 3) how each community’s location influenced the ability of each community to respond to changes. Five island communities, Anchor Point and Fogo Island/Change Islands in Newfoundland, and Chebeague Island, Monhegan, and Swan’s Island in Maine, were used as case studies and semi-structured interviews were conducted with people involved in fisheries and community development in each community in order to answer these questions.\nTwo particular aspects of fisheries policy and fisheries management were explored based upon observed trends from interviews: methods of limiting catch and licensing systems in each region. These themes connect to each other and relate to access to the resource. Each community had concerns about the ability of current and future harvesters and lobstermen to have economically viable access to the resource. In Newfoundland the relationship between island community development, fisheries policy, and fisheries management was perceived to be a top-down relationship; whereas in Maine it was perceived to be more integrated. Two of the most prevalent ways that communities directly responded to fisheries policy and fisheries management were either by changing management for their region or by creating new selling and processing capacity for their product. Typically the impacts felt in communities were from the cumulative nature of policy decisions. Respondents from each community felt that their location on an island was influential to their ability to respond to changes in the fisheries that they are dependent upon. Island community development, fisheries management, and fisheries policy have a complex relationship; this thesis explores those nuances.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".