Complementing Scientific Monsoon Definitions with Social Perception in Bangladesh
Bibliographic record
Abstract
Abstract The monsoon onset is a critical event in the Bangladesh calendar, especially for the domestic agricultural sector. Providing information about the monsoon onset for the past, present, and future has potential benefit for a country so vulnerable to changes in climate. But, when does the monsoon start? To produce any scientific information about monsoon onsets, lengths, and withdrawals, we first need to apply a monsoon definition to our data. Choosing a scientific definition is not such a simple exercise in Bangladesh. Different definitions lead to different monsoon onsets and thereby also monsoon lengths. If a climate application aims to provide information about the monsoon onset, then we need to understand how the people who might use this information perceive the monsoon onset. We then need to understand how their perceptions compare with previous scientific work. In this study we carried out a structured questionnaire in six rural regions around Bangladesh and asked the local agriculturists how they defined the monsoon and when they thought it started. It turns out that the agriculturists and previous scientific publications do not necessarily agree. Our results do not undermine previous scientific work on the monsoon in Bangladesh, but they do carry an important message about how we should design, implement, and evaluate climate applications in Bangladesh that encompass the monsoon onset.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".