Climate Variability and Fisher Adaptation in Lakshadweep: A Qualitative Study of Coastal Observations
Bibliographic record
Abstract
Coastal communities serve as vital observers of environmental change, and this study explores how small-scale fishers in the Lakshadweep Islands perceive and interpret climate variability through their lived experiences and traditional ecological knowledge. The research employed ethnographic and phenomenological approaches, conducting fieldwork across three inhabited islands: Kavaratti, Agatti, and Minicoy. Data was collected through semi-structured interviews, focus group discussions, participant observation, and participatory tools with 30 experienced fishers aged 25-70. Thematic analysis revealed three primary domains: perceived environmental changes, traditional indicators utilized by fishers, and adaptation strategies. Fishers consistently reported climate-induced alterations, particularly in monsoon patterns, sea conditions, and fish catch. Many continue to rely on traditional ecological knowledge to inform fishing decisions, but noted increasing unreliability of these indicators due to climate change. Adaptation strategies varied based on access to resources and experience, with younger fishers embracing technological tools and older fishers relying on accumulated knowledge. Community-based information sharing emerged as a significant resilience strategy. The findings underscore the importance of integrating local knowledge into climate adaptation policies and research. Recommendations include involving fishers in monitoring and planning, enhancing access to technology and training, strengthening livelihood support schemes, establishing community-based early warning systems, investing in participatory research, promoting climate education, and protecting coral ecosystems through local stewardship. The study highlights the value of indigenous knowledge in understanding and responding to climate variability, emphasizing the need for a hybrid approach that combines traditional wisdom with scientific innovation to build adaptive capacity in island communities like Lakshadweep.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| 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".