A History of Change and Reorganization: The Pelagic Longline Fishery in Gouyave, Grenada
Bibliographic record
Abstract
This paper traces the history of the pelagic surface longline fishery in Gouyave, Grenada, noting 4 major periods of change from pre-1985 to 2004. Reconstructed from document reviews, newspaper articles, oral history, and key informant interviews 1) the pre-1985 period was the time when the longline was introduced and popularized by the Cubans, 2) the period of institutionalization and technology development (1986-1990) corresponded to the strengthening of institutional arrangements and the initial improvements in technology, 3) the Coastal Fisheries Development Project (CFDP) of 1991-1999 was the period of international donor support and further technology change in longline construction, and finally 4) the 2000-2004 period marked innovation, training and fish quality control for export markets. The main point of the paper is that fisheries management is about the management of change. Fishery managers need to learn to deal, not only with technology change, but also with surprise and variability related to biophysical change (e.g., hurricanes), change in markets, and other external drivers such as international policies. Key considerations for managers to deal successfully with change include: learning from experience, capacity building, and the need to engage cooperatively with fishers and communities, the private sector, and non-governmental organizations.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".