Bibliographic record
Abstract
Commentators have examined the international law aspects of the new Canadian UGC exception, including its compliance with the Berne Convention and the WTO TRIPS Agreement. One issue that has not been considered much is whether this exception would serve as an ideal model for other jurisdictions that are undertaking digital copyright reform. Written for the Symposium on User-Generated Content under Canadian Copyright Law, this article uses Hong Kong as a case study to illustrate why the Canadian UGC exception, with appropriate modifications, can be — and should be — transplanted abroad.This article begins by discussing the efforts by the Hong Kong government to transplant copyright laws from abroad and its recent public consultation on the treatment of parody under the copyright regime. It further examines the benefits and drawbacks of legal transplants. Using the U.S. Digital Millennium Copyright Act of 1998 as a point of comparison, the article argues that the Canadian UGC exception would provide a timely and attractive model for legal transplant.This article then discusses specifically the UGC exception proposal I submitted to the Hong Kong government based on the Canadian model. Focusing on two key aspects of legal transplant — modeling and adaptation — the article identifies the key objections to the transplant of the Canadian UGC exception to Hong Kong, in particular those relating to the compliance with the TRIPS Agreement.The article concludes by recounting the Hong Kong government's report on the recent consultation, including its preliminary analysis of introducing a UGC exception into the Copyright Ordinance. Although this article strongly disagrees with this analysis, this Part takes seriously the government's international compliance concerns and offers seven additional modifications to further adapt the proposed transplant.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".