Coastal Ecosystem Services, Social Equity, and Blue Growth: A Case Study from South-Eastern Bangladesh
Bibliographic record
Abstract
By employing empirical and secondary data (qualitative and quantitative), this study demonstrates how social equity (with its three dimensions) can meaningfully address the conservation of the coastal social–ecological system (SES), without losing diverse ecosystem services (ES) in south-east coastal Bangladesh. Based on this proposition, this study assesses the available ES and identifies the drivers responsible for ES changes, arguing for the application of social equity for resource conservation. The findings show that communities along Bangladesh’s south-eastern coast use several ES for food, medicine, income, livelihoods, and cultural heritage. However, this valuable ecosystem is currently experiencing numerous threats and stressors of anthropogenic and natural origin. In particular, large-scale development activities, driven by the blue growth agenda, and neoliberalism policy, pose a risk to the local communities by degrading coastal ecosystem services. Escaping this situation for coastal natural resource-dependent communities in Bangladesh will require a transformation in the governance structure. Implementing the Small-Scale Fisheries (SSF) Guidelines that call for initiating policy change to deliver social justice to small-scale fisheries would help to address coastal ecosystem service conservation in Bangladesh.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".