Social Innovation in Small‐Scale Blue Food Systems: A Case Study of Oyster Harvesters in The Gambia, West Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The emerging “Blue Economy” and “Blue Growth” paradigms, focusing on economic growth, innovations, and environmental sustainability, have increasingly dominated discussions on marine and coastal development. However, in this discourse, the future of small‐scale blue food systems often remains underemphasized and increasingly uncertain. This paper explores the potential of social innovation approaches as tools to support a collective and inclusive transformation within blue food systems in the blue economy. We draw on a case study of a female‐led social enterprise in The Gambia—the TRY Oyster Women's Association (TRY)—to highlight the social innovation pathways for small‐scale blue food systems transformation. The study shows that social innovation through institutional changes, participatory governance, emerging institutional entrepreneurs, and financial resource mobilization and support facilitates effective natural resources management, environmental stewardship, and social and economic inclusion within small‐scale blue food systems. Importantly, the granting of TRY's exclusive user rights through a national Fishery Act has facilitated community engagement in sustainable management of the oyster shellfish and mangroves in The Gambia. Also, TRY promotes community empowerment and social cohesion through social learning and capacity‐building initiatives with financial and technical support from external partners enabling the association to thrive as a social enterprise. The paper underscores the significance of social innovation in steering successful transformation within small‐scale blue food systems, fostering environmental and inclusive resource management in the blue economy with applicability in similar geographical contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it