Group segmentation and heterogeneity in the choice of cooking fuels in post-earthquake Nepal
Bibliographic record
Abstract
Segmenting population into subgroups with higher intergroup, but lower intragroup, heterogeneity can be useful in enhancing the effectiveness of many socio-economic policy interventions; yet it has received little attention in promoting clean cooking. Here, we use PERMANOVA, a distance-based multivariate analysis, to identify the factor that captures the highest intergroup heterogeneity in the choice of cooking fuels. Applying this approach to the post-earthquake data on 747,137 households from Nepal, we find that ethnicity explains 39.12% of variation in fuel choice, followed by income (26.30%), education (12.62%), and location (4.05%). This finding indicates that ethnicity, rather than income or other factors, as a basis of policy interventions may be more effective in promoting clean cooking. We also find that, among the ethnic groups in Nepal, the most marginalized Chepang/Thami community exhibits the lowest intragroup diversity (Shannon index = 0.101) while Newars the highest (0.667). This information on intra-ethnic diversity in fuel choice can have important policy implications for reducing ethnic gap in clean cooking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".