Equalizing Health and Education: Approach of the Twelfth Finance Commission
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 adjustment 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 selected states. In determining these grants, the TFC had kept the equalization principle in perspective and has not underwritten deficiency in expenditure if it arises because states accord less than average priority to the concerned sector. Recommended grants however only partially meet the requirement of resources for these sectors. For meeting the needs fully, much larger amounts would be involved. TFC’s initiative should be taken only as a beginning that requires supplementation by plan grants. After gaining experience in implementing these grants, larger grants and a more comprehensive approach can be developed.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".