The weather does not support farmers: an exploratory qualitative study in Kavre district, Nepal.
Bibliographic record
Abstract
Kavre district, Nepal, is highly vulnerable to climate change impacts, including increases in erratic rainfall, drought, floods, and landslides. As gender roles, culture, age, physical and physiological characteristics increase, mainly Nepalese women's and children's, health risks associated with climate change and air pollution, listening to and learning from women is critical. This study explores women's perspectives and lived experiences concerning climate change, consequent adverse impacts on agriculture and health, and ongoing adaptation and mitigation strategies. Assessing perspectives and lived experiences related to climate change can offer opportunities to explore understanding, local beliefs, experiences with adverse impacts and adaptation. We used a descriptive qualitative approach. An equal number of focus group discussions (FGDs, n=8) and key-informant interviews (KIIs, n=8) were conducted. Purposive and snowball sampling were used to recruit participants. Four research assistants with public health backgrounds and climate change training were employed to assist with this work. All interviews were conducted in the Nepali language using an interview guide. All KIIs and FGDs were audio-recorded and transcribed verbatim in Nepali. Data were analyzed in NVivo 1.7 using content analysis. Forty-two of the 48 participants identified as women. The largest proportion of participants was aged greater than or equal to 50 years (18/48), had no formal education (21/48), and were either older women (>55 years) (13/48) or mothers of children younger than five (11/48). Three main topical areas emerged from the FGDs and KIIs: (i) the winds of change, (ii) the unpredictability of weather, and (iii) acting locally. The study provides insights into how women and children in rural communities in a Nepali hill district experience, adapt and mitigate climate change impacts. These findings can help inform the development of interventions to better address women's and children's needs and concerns, essential to promoting well-being and reducing impacts exacerbated by 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".