Growing social vulnerability in the river basins: Evidence from the Hindu Kush Himalaya (HKH) Region
Bibliographic record
Abstract
Vulnerability is a set of conditions of people that is derived from the historical and prevailing socio-economic, cultural, environmental and political contexts along with understanding future scenarios, especially for climate change. This study aimed at better understanding the nature and types of socio-economic drivers and social vulnerabilities in the context of increasing climatic stresses in four river basins in the Hindu Kush Himalaya (HKH) region. A multidimensional, contextual and integrated approach has been applied using participatory qualitative tools and techniques to identify major socio-economic drivers and conditions along with climatic factors in upstream, midstream and downstream of the river basins. In upstream and midstream region, people’s livelihood is dependent on subsistent agriculture, horticulture, pastoralism and tourism while in downstream, agriculture and fisheries are the major livelihood options. Climate sensitive natural resources based livelihoods are severely affected across the river basins. Poor and marginal population are not able take adequate adaptation measures due to lack of capacities, poor access to resources, services, information, which push them into greater vulnerability. The vulnerable groups in all four river basins are marginalized sections who are conditioned by economic classes, gender and social norms and living in geographically underdeveloped areas. For instance, poor, women, religious/ ethnic minorities, subordinate caste groups, char dwellers. Poor governance and the lack of access to resources and services have made the situation worse. All these factors are enhancing social vulnerability across the basins and study sites. Social protection measures, enhancement of human capitals and livelihood diversification with pro-poor and gender responsive adaptation and socially inclusive policy are needed to address growing social vulnerability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".