Website: www.mse.ac.inEqualizing Health and Education: Approach of the Twelfth
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
Service standards in the provision of health and education in the states in India are low on average and also characterized by large inter-state disparities. These disparities are due to differences in fiscal capacity, differences in revenue effort and differences in priority accorded to the concerned sectors. The transfers from the central to state governments in many federations are guided by the equalization principle. Two important examples are Canada and Australia. When unconditional transfers are made, equalization transfers aim to neutralize deficiency in fiscal capacity but not that in revenue effort. Sometimes adjustments affecting cost and need factors may also be accommodated. Both in Canada and Australia, apart from general purpose and unconditional transfers, there are also specific purpose transfers. Considering the fact that it is important not only to improve the average levels of provisions of health and education services, but also to reduce disparities across states, the Twelfth Finance Commission has recommended special grants for health and education to
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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