Government Copyright in School Textbooks and the Fundamental Right to Education
Bibliographic record
Abstract
The pandemic has compelled us to undertake many activities online, and education has been no exception. There is no gainsaying the fact that education has the potential to be a significant means to counteract inequalities. And yet, the manner in which online education has been delivered in recent times has brought into stark relief, and further exacerbated, the digital divide and widening socio-economic inequalities in the country. Only around a quarter of Indian families have access to the internet, according to estimates. This percentage reduces to 15% in rural homes. As usual, marginalised, rural, and destitute communities have been hit the hardest. There have even been multiple reported cases of suicides by students in the country on account of lack of access to education during the ongoing pandemic. There should, therefore, be a renewed and urgent emphasis on the need to make education, online or offline, more inclusive. Equitable access to learning material and textbooks for education constitutes a basic requirement for the realisation of this goal. However, access to textbooks in India has been riddled with distribution problems at the best of times, and the pandemic has only increased the impact of differential access. Against this backdrop, this paper explores the issue of the government’s copyright ownership in State Board textbooks and its implications for access to knowledge and education.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".