Social-ecological Dynamics and the Effects of Bonding Social Capital on Local Fish Marketing in Grenville, Grenada
Bibliographic record
Abstract
Grenville, the second largest fishing centre in Grenada, share characteristics typical of small scale fisheries across the eastern Caribbean and further afield.A major fishery involves small tunas and tuna-like fishes.Approximately 50 boats, typically with a crew of two, troll daily inside and along the edge of the island's extensive shelf, landing on average nearly 400 metric tonnes of fish annually.Sixty percent of these landings are usually blackfin tuna (locally known as 'bonita' or 'common tur') and skip jack tuna.Over the last seven years, this fishery and particularly its marketing system, have been plagued with perturbations, both idiosyncratic and covariate.In this paper, I explore some critical social-ecological factors that cause or contribute to these perturbations.I highlight how bonding social capital between fishers and unemployed youths (two key categories of stakeholders in the fishery) helps them to cope with some of these perturbations, as well as adding fire to the flame.This paper is part of larger doctoral research on the governance of small-scale fisheries in the eastern Caribbean.The findings here are based upon information collected through key informant interviews, participant observations, and informal interviews during a one year period of residence (July 2010 -June 2011) in the fishing community of Grenville.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".