Back to the Future: The Digital Millennium Copyright Act and the Trans-Pacific Partnership
Bibliographic record
Abstract
The Trans-Pacific Partnership (TPP) is a trade agreement, which seeks to regulate copyright law, intermediary liability, and technological protection measures. The United States Government under President Barack Obama sought to export key features of the Digital Millennium Copyright Act 1998 (US) (DMCA). Drawing upon the work of Joseph Stiglitz, this paper expresses concerns that the TPP would entrench DMCA measures into the laws of a dozen Pacific Rim countries. This study examines four key jurisdictions—the United States, Canada, Australia, and New Zealand—participating in the TPP. This paper has three main parts. Part 2 focuses upon the takedown-and-notice scheme, safe harbours, and intermediary liability under the TPP. Elements of the safe harbours regime in the DMCA have been embedded into the international agreement. Part 3 examines technological protection measures—especially in light of a constitutional challenge to the DMCA. Part 4 looks briefly at electronic rights management. This paper concludes that the model of the DMCA is unsuitable for a template for copyright protection in the Pacific Rim in international trade agreements. It contends that our future copyright laws need to be responsive to new technological developments in the digital age—such as Big Data, cloud computing, search engines, and social media. There is also a need to resolve the complex interactions between intellectual property, electronic commerce, and investor-state dispute settlement in trade agreements.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".