Understanding the Local Livelihood System in Resource Management: The Pelagic Longline Fishery in Gouyave, Grenada
Bibliographic record
Abstract
There is a need to include social objectives in fisheries management, and this paper focuses on one set of social considerations, those regarding livelihood. We pay particular attention to sustainable livelihood strategies, the importance of commercial pelagic longline fishing for the entire community livelihood system, and implications for management. Field data were obtained between December 2002 and March 2004 in Gouyave, Grenada, using participant observation, semi-structured interviews, and a quantitative survey. The economic base (fishing and agriculture) of the community is both unpredictable and seasonal, therefore individuals and households engage in diverse strategies to secure their livelihood. Three livelihood strategies were deemed important: 1) livelihood diversification, developing additional sources of income from agriculture, wage labor, and trade work, 2) fishing diversification, learning to switch to alternative gear and species, and 3) the availability of an informal "social security net" involving cash and in-kind assistance. These strategies help to spread the flow of income and food during lean times and across seasons. A major management implication is that fishery managers need to pay attention to the multi-species nature of fisheries and to the importance of livelihood diversification, including reliance on other economic sectors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".