Climate change research, capacity building and communication on climate extremes over South Asia
Bibliographic record
Abstract
Climate change is a global issue that significantly impacts various regions, including South Asia, which is particularly vulnerable to climate extremes. Extensive research is required to address the complex interplay between climate change and extreme weather events in South Asia (Bangladesh, Nepal and Pakistan). This study presents a case study of an Asia-Pacific Network for Global Change Research (APN) project focusing on climate change research, capacity buildingand science-to-policy communication on climate extremes in South Asia. Climate change research emphasises the importance of research to understand the changing patterns and impacts of climate extremes in the region. It underscores the need for robust scientific methodologies, data collectionand analysis to generate reliable evidence for policymakers and stakeholders. The capacity building efforts involve training programmes, workshopsand knowledge-sharing platforms, which are critical to enhancing the capabilities of local researchers, institutionsand communities in conducting climate change research and developing adaptation and mitigation strategies. The science communication includes disseminating the study’s findings to stakeholders, including policymakers, researchers, communities, mediaand civil society organisations. Overall, collaborative efforts between South Asian countries are important for climate change research, capacity buildingand science-to-policy communication to build resilience and mitigate the impacts of climate change.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".