Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper analyzes the determinants of rural poverty in India, contrasting the situation of the Scheduled Caste (SC) and Schedule Tribe (ST) households with the non-scheduled population. The incidence of poverty among SC and ST households is significantly higher than non-scheduled households. Using a probit decomposition analysis, we decompose the difference in the poverty rates between the scheduled castes (or tribes) and non-scheduled households into a part explained by the differences in characteristics and a part explained by \nthe differences in probit coefficients. The paper finds that for SC households, differences in characteristics explain the gap in poverty rates more than differences in coefficients; while for ST households, it is the reverse. Differences in educational attainment explain about one quarter of the poverty gap for both social groups. Occupational structure strongly matters in \ndetermining the poverty gap for both SC and ST, as does differences in returns to individual occupations. While poverty rates are not very different between SC and ST households, the analysis suggests that the underlying factors for the higher incidence of poverty in these social groups are to a large extent different.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 it