Exploring Values and Beliefs in a Complex Coastal Social-Ecological System: A Case of Small-Scale Fishery and Dried Fish Production in Sagar Island, Indian Sundarbans
Bibliographic record
Abstract
The survivability of the small-scale fishery and dried fish production in Indian Sundarbans, despite increasing threats posed by climate, environmental, economic, and policy drivers, suggests that they possess certain unique strengths and capabilities. One thread of these strengths is connected to the fact that Sundarbans’ fishery system is strongly anchored in the values and beliefs of the local fishing communities. There is, however, limited empirical information available on the prevailing individual and collective attitudes, expectations, traditions, customs, and, above all, values and beliefs that strongly influence local fishing communities of Sundarbans. This manuscript aims to address this gap by drawing on qualitative data to (1) map the nature of values and beliefs associated with the Sundarbans’ Sagar Island fishing communities who are engaged in small-scale fishery and dried fish production; and (2) highlight the contributions of values and beliefs to the small-scale fishery and dried fish production systems of Sagar Island. Our study reveals that historical factors such as the patriarchal and patrilineal system prevalent in the Indian Sundarbans as well as the current drivers, including environmental and social-economic changes, create inconsistent values and beliefs among male and female members of its society. Issues around values and beliefs are heavily influenced by social-ecological realities comprising material, relational and subjective dimensions. They can range from being strictly personal to largely community-oriented as they are shaped by realities of gender, class, power dynamics, and politics. Values and beliefs are fundamental to human perception and cognition but often get neglected in mainstream literature covering human dimensions of resource management. Our research adds weight to the theoretical and place-based understanding of the contributions of values and beliefs to the small-scale fishery and dried fish production systems. We learn from the case study that values and beliefs can act as mirrors, reflecting the current as well as future realities of small-scale fisheries and dried fish production systems and provide important directions for sustainability and viability of the entire social-ecological system that hosts this sector.
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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 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".