Income Levels and Transition of Cooking Fuel Among Rural Poor in India
Bibliographic record
Abstract
More than two-thirds of the population lives in rural India. Even after six decades of independence and two decades of economic liberalization, it remains the bitter truth. This study assumes greater significance, because, majority of the rural people are still dependent on biomass fuel for cooking. A reduction in this could help India in greatly reducing the indoor as well as environmental pollution levels; thus helping in containing global warming. It would also help in controlling the health hazards caused due to the indoor pollution in the rural economy; which in effect would help the government in reducing the spending on public health. To make this happen, the government should make the distribution system of kerosene and Liquefied Petroleum Gas (LPG), efficient. This study could be useful not only to India, but to many other economies that are on the threshold of transition; where majority of the population, still lives in the rural areas, and are predominantly dependent on agriculture for their livelihood. This study was undertaken with the objective of analyzing the socio-economic conditions of rural poor in India with respect to their primary energy consumption viz. cooking fuel and impact on health. The study conducts a questionnaire based survey on demographic, economic, and perceptible parameters on modern fuel such as kerosene and LPG; using logit model to identifying variables useful for the study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".