Facilitating Educational Needs in Digital Era: Adequacy of Fair Dealing Provisions of Indian Copyright Act in Question
Bibliographic record
Abstract
The recent educational policy of India has recognized the use of Information and Communication Technologies (ICTs) to meet the educational needs in the digital era. But some of the objects of the policy like making available suitable e‐content, preparing knowledge modules, facilitating e‐learning etc. look far‐off, given the extant set of fair dealing provisions in the Indian Copyright Act. Against this backdrop, this paper delineates the doctrine of fair use and that of fair dealing followed by an account of international regime of fair use in copyright specific to education. Subsequently, it analyses the concerned fair dealing provisions of Indian Copyright Act and finds that they are too narrow and inadequate to foster the educational needs. The paper concludes with the view and suggestion that India needs to step beyond the fair dealing doctrine and head towards the doctrine of fair use through complementary role of the legislature and the judiciary on the lines how it happened in Canada. Over and above, there is a need to undertake some incentive‐oriented policies encouraging copyright owners to forgo their commercial interests to some extent for the sake of 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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".