Bibliographic record
Abstract
This paper examines the widespread perception in India that the country has an acute shortage of one million teachers in public elementary schools, a view repeated in India’s National Education Policy 2020. Our analysis of government’s DISE data shows that the median number of enrolled pupils in India’s 1.03 million public elementary schools is a mere 63 pupils, and that many tiny schools have surplus teachers. Adjusting those against the number of teacher vacancies yields a net deficit of only a quarter million teachers. Secondly, removing fake student enrolments converts this net deficit into a net surplus of about one hundred thousand teachers. Thirdly, we show that if government does its promised fresh recruitment to fill the supposed one-million teacher vacancies, the already modest mean pupil-teacher-ratio of 25.1 would fall to 19.9, permanently increasing fiscal cost by USD 8.7 billion per year in 2019–20 prices, which is higher than the individual GDPs of 50 poorest countries that year. The paper raises questions about minimum viable school-size, teacher-allocation norms, permissible maximum pupil teacher ratios, and teacher deployment.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".