Education Financing in India: Navigating the Neo-Liberal Shift
Bibliographic record
Abstract
Abstract: The focus of this paper is on analysing the changing nature of sources of education finance in India. The onset of a wave of liberalisation and privatisation in the last quarter of 20th century has shifted the policy orientation of developed and developing countries wherein more importance is accorded to private sector while the state’s role is shrinking in every economic activity including education. In line with the global trends, India has progressively been withdrawing its funding in social sector including education and providing more space to private service provider since the 1980s. Using various indicators related to different sources of educational financing, it is observed that privatisation wave is taking place at all levels of education- elementary, secondary and higher. The pace is fastest in case of higher education. Further, inter-state comparison of the increasing role of household level financing of education indicates that, in general, economically well-off states are leading the poor states in this trend. The shift in the sources of education finance (from public to private) may have far reaching implications for the economic development and smooth social transformation especially in a heterogeneous society like India.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".