Conflict of Resource Use Among Different Livelihood Group in Coastal Villages of South-Western Bengal Delta, Bangladesh
Bibliographic record
Abstract
People, ecosystems and resources are three crucial components for understanding resource use conflicts. This study examines resource use conflicts in two coastal villages of south-western Bangladesh, where access to resources are essential to rural livelihoods. Resource utilization conflicts can emerge when interests and needs of different users groups are incompatible or denied by each other. Considering these issues, this study has taken as an effort to explore the issues, reasons, stage, scale and impact of conflicts. Relevant data were collected through questionnaire survey, Focus Group Discussions (FGD) and Key Informant Interview (KII). The study found that rapid encroachment of crop land into shrimp farming, contrasting dynamic occupational practices, human made over use or overstrain of natural resources combined with environmental degradation and climate change, pose serious threat to human security. These rapid, and mostly unexpected changes provoke conflicts among the dominant resource user groups. Moreover, driver of conflicts and typological classification were addressed to make them comparable in the sense which one requires the most attention according to the predicted scale and urgency of impact. Conflict management strategies were discussed by four building blocks which might be a remarkable part of conflict prevention in the study area.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".