Gender dynamics and climate variability
Bibliographic record
Abstract
The Hindu Kush Himalayan region is a highly diverse and dynamic area geographically and culturally. Scientific evidence indicates that climate change affects the local socio-ecological systems with potential to cause long-term transformations directly or indirectly. This, coupled with other drivers, especially changes in demography, livelihood resources, gender relations and socio-economic structures, have aggravated the vulnerabilities of the communities to changes in climate. This study, set in three different elevations in the state of Uttarakhand, explores and unpacks the dynamics relating to the roles and responsibilities that govern rules, norms and practices that are defined as gendered institutions. The study modifies the IAD framework through a gendered lens to portray how patterns of gender relations within different socio-economic groups are impacted with changing climate in the study sites with respect to two main activities: accessing water and agriculture-related activities. Using empirical evidences, the study indicates that across study sites, men and women perceive climatic variability concurrently yet differently. Further, the disaggregated information collected on gender, class and caste showed how the gender-selective nature of the division of labour represents a strong dichotomy between ‘reproductive’ and the ‘productive’ activities. This was largely manifested in the existing social conformism in all the study sites, which presents critical challenges influencing social relations, position and mobility, predominantly for women and men from the marginalized and lower-caste communities of Uttarakhand.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".