Bibliographic record
Abstract
In recent years, Australia, Canada, the United States, Ireland, the United Kingdom and other members of the European Union have been busy exploring ways to modernize their copyright laws. In many of these jurisdictions, new copyright exceptions have been introduced or proposed to promote internet users’ access to digital content. Meanwhile, the copyright industries have uniformly opposed the introduction of these exceptions. This chapter scrutinizes seven of the industries’ most widely used arguments. Drawing on examples from digital copyright reform in Hong Kong and other jurisdictions, the chapter explains why the industries’ arguments have thus far been unconvincing. It also calls on policymakers and legislators to critically evaluate these arguments, lest they lead to wrong policy choices that harm internet users and the public at large.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".