Of Square Pegs and Round Holes: Towards a New Paradigm of Database Protection
Bibliographic record
Abstract
This article looks at the question of the applicability of copyright law to the protection of databases. It features a detailed discussion of the EU Database Directive, which is the only comparable legal framework for the protection of databases. It then discusses some problems that the EU Directive encounters vis-à-vis public interest concerns, and outline why the EU Directive is unable to strike the right balance, both in principle and in practice. Next, it briefly studies database protection law as it exists in the United States, Australia, Canada and finally India, following which the need for protection of databases in India is assessed. Finally, a basic alternative framework for the legal protection of databases is proposed, seeking to balance the interests of database generators and those of the public at large. The authors argue that databases should be protected with reference to principles of the law of unfair competition, which recognizes that a balance needs to be struck between the interests of owners and the public. The authors also suggest the registration of databases with a governmental authority (similar to the trademark registration process) so as to properly delineate the scope of commercial exploitation that the database owner intends. Further, an argument is made for compulsory licensing provisions.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.022 | 0.056 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".