Ecosystem changes and community wellbeing: social-ecological innovations in enhancing resilience of wetlands communities in Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
The feedback relationships between resource-dependent human communities and their local ecosystem services may result in an undesirable SES dynamic where community wellbeing continually deteriorates. Yet, very little is known about the mechanisms that can facilitate reordering people’s relationship with ecosystem for sustained wellbeing. We argue that innovative approaches built upon local strengths are more likely to succeed in such an endeavour, and multi-level implementation can help sustainability and community wellbeing. The objective of this paper is to give an account of local people’s wellbeing in relation to ecosystem services and their changes, the drivers that change the ecosystem services and impact wellbeing, and the role of innovation in enhancing adaptability and SES resilience to changes and disturbances. We conducted our field research during 2014–2015 in a wetland region of northeastern Bangladesh, utilising Case study and participatory research methods. Our empirical investigation in selected wetland communities has revealed that: (i) community wellbeing and wetland SES resilience are subject to erosion due to multiple drivers of change operating at different temporal and spatial scales; (ii) the feedback relationships among these multiple drivers often adversely affect community wellbeing; and (iii) innovative strategies built on local strengths can help reorder people’s relationships with local ecosystems, restore community wellbeing, and enhance local sustainability. The key determinants of such reordering include entrepreneurial skills, knowledge, learning, and networking abilities of the locals. Intervention strategies should therefore pay more attention to the locals’ ability to innovate and adapt to ecological changes, especially through new or shifted livelihood initiatives.
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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 | 0.001 | 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 it