Drivers of social and ecological change in floodplain rice-fish systems in Bangladesh
Bibliographic record
Abstract
This thesis examines the role of a diversity of drivers on the development of institutions at three case study sites encompassing different management strategies of rice-fish floodplain systems.Objectives of the study were to document drivers of change and institutional responses at selected rice-fish farming sites; to document the key cross-scale institutional linkages at selected rice-fish sites; and to build scenarios to explore farmer/fisher perceptions of the future of ricefield fisheries and other resources, using information on key drivers influencing change in the local area.Bangladesh floodplain beel ecosystems (perennial floodplain depression) are complex and dynamic.They are extremely important in providing fish protein for the rural poor, including a class of mobile, landless fishers.Multiple-use areas beels are valued by different user groups for different reasons.These systems are experiencing widespread alterations, impacted upon by a number of different forces or drivers originating from a complex interaction of social, cultural and environmental factors.These forces have historic roots in both the pre and post colonial and contemporary contexts of Bangladesh.The management institutions in place are created or modified in response to these drivers, in conjunction with the social-political interactions of local governance; different levels of economic and political power; and elite capture of resources.The key drivers of changing agro-ecological context and conditions were: demographic and migration (mobility) changes, increased emphasis on decentralization, the privatization of provisioning and forces of globalization are challenging and redefining the meaning and methods of participation in Bangladesh civil society.Alternate resource governance strategies included private floodplain culture-based fisheries (which received widespread government promotion), and self-organized forms of management, including community-based and co- managed fisheries.Based on scenario exercises, many groups of local people felt excluded from the broader management process.They had different perceptions and narratives on the impacts of different schemes and the future of rice-fish systems.The inclusion of these insights and other knowledge sets are necessary for the future development of sustainable floodplain resource use policy.Scenario exercises elucidated people's concern with changes in the landscape and resulting impact on the future of fishing and farming.These in turn shape their sense of identity and the future world of their children.Bhatibarerarepar, Dariakandi, Takevita (Solimari/Gheramari), Chandpur (Chandpur Beel) and Melandi (Beel Mail) who all came out, participated and expressed their views and ideas.
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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 | 0.000 | 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".