An Analytical Examination of Research Trends in the Indian National Education Policy 2020: A Scientometric Approach
Bibliographic record
Abstract
Education policies are used by nations across the world to regulate the education systems of their countries. Effective drafting and implementation of the policies will put the educational sphere of the nation on the global radar. Reviewing multiple publications related to the Indian National Education Policy (NEP) 2020, this work aims to scientifically evaluate research related to the 2020 NEP, performed from 2019 to 2022, as against the inefficient manual ways of evaluating bibliometric datasets. A scientometric approach was employed to analyze the bibliometric dataset extracted from the Scopus database with the aid of VOSviewer and R tools. Different stages and main themes of the NEP-2020 were identified. Moreover, the most prolific authors, highly contributing organizations, country contributions, and collaborations, as well as keyword analysis, were carefully evaluated. A total of 273 authors were identified to have contributed to the Indian NEP-2020, out of which 154 have at least 1 publication and a citation count. 91 out of the 233 contributing organizations have at least 2 citation counts, and the top contributing country is India, followed by the United States of America, Britain, Australia, Canada, and France. The findings of this analysis will give concise information about the trends of the 2020 NEP to researchers, the government, and the general public at large. The future scope of this work includes comparing the Indian education policy to that of other top developing and developed nations.
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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.144 | 0.230 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".