Gender, Social-Ecological Regime Shifts and Governance in Small-Scale Fishery Commons of Chilika Lagoon
Bibliographic record
Abstract
A social-ecological systems perspective can be used to analyze the connections between human and ecological components in complex and dynamic natural resource commons systems, such as fisheries. When fishery commons experience regime shifts, which are rapid changes in an ecosystem’s structure and function that are often difficult to anticipate and either extremely costly or impossible to reverse, there are social and ecological consequences. A gendered lens is crucial in analyzing these shifts as gender roles in fisheries have been constructed by society, dictating the differences between men and women’s tasks, responsibilities and resources thus, changes in the system impact men and women in different ways. Women are often seen at the receiving end of rapid environmental change without having much control over how these changes were triggered in the first place. The purpose of this research was to examine through a gendered lens, the social-ecological regime shifts (SERS) in coastal fishing communities in Chilika Lagoon in India. The objectives were to understand the processes of social-ecological regime shifts from the perspective of women compared to those of men, examine how women and men respond and adapt to the impacts of social-ecological regime shifts and finally, examine how the implications of a gender sensitive approach to understanding social-ecological regime shifts can contribute to novel governance approaches in dealing with SERS. Through the use of semi-structured interviews and focus groups, it is apparent that SERS have unique impacts on the women in Chilika Lagoon as they deal with the consequences of rapid change. There are some adaptations taking place to deal with these impacts but a more gender sensitive approach to the governance, which encapsulates the needs of women and focuses on women’s empowerment, is necessary to create a more intersectional and holistic governance system that benefits both the men and women living in this fishery commons.
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".