The role of family in shaping adaptation and adaptive capacity in small-scale fishing communities: The yellow clam fishers in Uruguay
Bibliographic record
Abstract
Small-scale fisheries (SSFs) face numerous challenges, including resource overexploitation and precarious livelihoods due to limited or ineffective formal and institutional governance systems. In addressing the multifaceted challenges SSFs confront, such as climate change, biodiversity loss, and livelihood security, understanding their adaptive capacity becomes fundamental. Various social factors, including family dynamics, influence adaptive capacity. This paper presents an in-depth case study from Uruguay, examining the role of families in the yellow clam SSFs' adaptive capacity. It explores the influence of family ties and their impact on adaptation processes. The study draws on diverse datasets to highlight families' role in building adaptive capacity within SSFs. We find that family networks are a significant driver of other types of important social networks in communities (e.g., labor, governance, and knowledge). Additionally, family structures within communities influence key adaptive processes, such as the marketing of harvest within value chains. Our findings emphasize the significance of family as local, informal institutions and networks to strengthen capacity to manage diverse stressors and resources. Empirically, the paper sheds light on the intricate web of connections that are pivotal for the functioning of fisheries communities and the complex interplay between fisheries and family dynamics, and our work is important for informing policy interventions aimed at enhancing adaptive capacity through existing social capital. • Family is an important informal institution, which influences adaptation in rural, small-scale fishing communities. • Family ties are strong predictors of other networks within communities (e.g., governance , labour, and knowledge). • Family influences key adaptive processes, such as the degree of adoption of an innovation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".