Bibliographic record
Abstract
In Parts I and II of this Paper, the author analyzes the legal protection of databases first in international treaties, in particular the Berne Convention and the WTO TRIPS Agreement, and second under national and regional copyright, sui generis, or other (e.g., tort) law in Europe (both the European Directive on the legal protection of databases of 1996, which was under review, and a number of relevant national laws), the United States, and a number of foreign jurisdictions (Australia, Canada, China, Nigeria, Russia, and Singapore). In Part III, the author provides a critical analysis of the effort to expand the legal protection of databases from both theoretical and empirical perspectives. In his conclusion, the author suggests three paths for the future evolution of the protection of databases at the international level.
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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 | 0.027 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".