The Semi-Privatization of Digital Copyright Regulation: The Politics of Automated Filtering and Platform Immunity in Canada, the European Union, and the Trans-Pacific Partnership
Bibliographic record
Abstract
Recent reforms to digital copyright enforcement have given platform intermediaries and large copyright holders the power to sanction billions of underrepresented users worldwide. The automated monitoring, filtering, and removal of user-generated content has mirrored other forms of machine-based decision making, as it provides legal authority to algorithms and privatizes control over legal expression. While there is much debate on the effectiveness of current enforcement methods, there is still much to understand about the politics that influence these changes and the legal and policy frameworks that lead to machine-based decision making. \n\nTo fill this gap, this study explores the recent policymaking discourses that have influenced public narratives of automated filtering and the legal outcomes of related regulatory debates. I present three case studies of international and national reforms in one specific area of internet policy: intermediary liability law. These case studies include the Trans-Pacific Partnership in the United States (2016), The Canadian Copyright Modernization Act (2012), and Article 17 of the new Directive on Copyright in the Digital Single Market in the European Union (2018). I have analyzed hundreds of pages of government documents, including hearing transcripts, stakeholder submissions, and government reports to ascertain how reforms to digital copyright enforcement have developed and what this documentary evidence discloses about the politics and the geopolitics that have influenced these changes. Additionally, I analyze the legal and policy frameworks that lead to machine-based decision making, and the implications of automated content controls on social welfare and human rights.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".