Why Gher Cannot Serve as a Climate Change Adaptation Model: A Case Study on Shrimp-Rice Intercropping in Joymoni, Mongla, Bangladesh
Bibliographic record
Abstract
With the intricate and growing impacts, it has become obvious that adaptation is one of the keys to combat with climate change in Bangladesh. Many strategies are implemented in response. In 2014, the Government of Bangladesh identifies a four decades of intercropping method of cultivating paddy, shrimp and fin fish, called gher as an adaptive model. Massive scale commercial shrimp farming that began in the90 s has made shrimp the second largest export item by volume. Researches show that gher already caused much harm to croplands and waters affecting vegetation, livestock and livelihoods of the people. It continues to degrade the environment, estuaries, forests, and biodiversity. It furthers the existing threats of Sea Level Rise, salinity intrusion, and erosions. Taking Vulnerability (Adger, 2006) and Theory of Access (Ribot & Peluso, 2003) as research framework, this Human Geography study explores the limitations of gher as an adaptive model in Joymoni, Mongla, Bangladesh.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".