Bibliographic record
Abstract
Given the long history of copyright reform battles in Canada that date back to the 1880s, there was little reason to think that when the Canadian government launched a public consultation on digital copyright issues in the spring of 2001 that the effort would mark the beginning of a dramatic shift in approach in which thousands of people would become politically engaged in copyright policy and pressure the government to rethink how it struck the copyright balance. Yet a decade of public debate culminated in copyright reforms that were among the most user-friendly in the world, emphasizing user rights and featuring an expansion of fair dealing, a host of new consumer exceptions, innovative new technology focused exceptions, limitations on liability for noncommercial infringement, and notable safeguards for user privacy in Internet service provider liability rules. This paper explores the stunning evolution of Canadian copyright law, highlighting how technology, the Internet, and a growing awareness of the wider implications of copyright policy for education, commerce, creativity, and everyday consumer uses sparked a users’ rights movement that helped shape copyright policy long before the better-known digital rights successes involving SOPA/PIPA in the United States and the Anti-Counterfeiting Trade Agreement in Europe. The Canadian copyright story demonstrates how individuals were able to leverage social media and a strong scholarly foundation to not only stop legislative proposals, but to proactively forge a positive copyright agenda that placed users at the center of policy development.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".