Unveiling Exclusion in The National Education Policy 2020: Impacts and Implications for Indian Education
Bibliographic record
Abstract
The National Education Policy 2020 promotes diversity, equity, and quality learning to reform India’s education system. This paper critically examines NEP 2020 compared with the National Policy on Education 1986 to assess its potential in addressing disparities related to gender, caste, socioeconomic status, and regional inequalities. Using qualitative content analysis of policy documents and reports, the study highlights gaps in implementation, especially for marginalised groups, linguistic minorities, and the digital divide. While NEP 2020 proposes reforms such as a flexible curriculum, regional language emphasis, technology integration, and teacher education improvements, its success depends on overcoming infrastructural and economic challenges. Comparative perspectives from Finland and Canada provide insights into public funding, multilingual policies, and evaluation frameworks. The paper concludes with recommendations for strengthening infrastructure, bridging digital gaps, and monitoring frameworks to ensure inclusive education in India.
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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".