NEP 2020 : reversing the trend of brain drain from elite schools of India?
Bibliographic record
Abstract
The yearning for high quality education and better employment opportunities has compelled millions of bright and talented Indian students to explore better prospects overseas for years. Several studies indicate that in the last two decades, more than half of the high school toppers have left the country to countries such as UK, USA and Canada because of the many push factors in India. To crackdown this trend, India has recently proposed few transformative re-forms in the National Education Policy( NEP 2020) \n \nThe purpose of this thesis was to investigate whether the NEP 2020 reforms in higher education would limit brain drain from elite schools of India. The response of guidance counsellors from elite private schools of India was used to gather the primary data for this study. Based on the theoretical framework, previous studies on human capital, student mobility and guidance counselling were examined. The research questions were selected on the basis of the theoretical study related to education policies in higher education and their link to human capital theory and international student mobility. In addition, guidance counselling theories were included to understand counselling practices in students’ transition to tertiary education. As the study was commissioned by an EdTech company which provided support for college counselling, the study also explored if the current features in their tool was equipped to handle the new demand for Indian universities in light of the reforms in NEP 2020. \n \nThe research involved collecting data using a survey and group interview of counsellors and for this sequential mixed method was adopted. Quantitative data was collected by surveying counsellors with close ended questions while qualitative data was gathered by interviewing counsellors with semistructured questions. 47 counsellors from elite schools of India participated in the survey. Further clarification was sought to gather deeper insights on the future trends in Indian student mobility by interviewing three of these counsellors. Overall, the results indicate that, the brain drain is most likely going to reverse because of the reforms proposed in NEP 2020 and as a result the country stands to benefit eventually from the human capital gain. However, further research is required to determine other externalities which could influence student mobility trends.
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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 | 0.002 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".