Bibliographic record
Abstract
Around the world, the focus of copyright policy reform debates is shifting from the protection of copyright owners' rights towards defining their appropriate limits.There is, however, a great deal of confusion about the legal ontology of copyright "limits," "exceptions," "exemptions," "defenses," and "user rights."While the choice of terminology may seem to be a matter of mere semantics, how we describe and conceptualize lawful uses within our copyright system has a direct bearing on how we delimit and define the scope of the owner's control.Taking seriously the role of rhetoric in shaping law and policy, this Paper critically examines the recent embrace of the language of "users' rights" to frame fair use, fair dealing, and other non-infringing acts.This terminology has been adopted to varying degrees by courts in Canada, Israel, and the United States and is increasingly employed by public interest advocates and policy-makers at the domestic and international level.In this Paper, I ask whether the rise of "user rights," thus cast, is a positive development that will help to rein in some of copyright's excesses, advancing the cause of content users and the public at large-or whether it is, perhaps, something of a false friend.Drawing on lessons from critical legal theory, I caution that "rights" may be a double-edged sword with the potential to undermine or obstruct the public interests, social values, and relationships that should inform copyright's development in the digital age.As a rhetorical tool, "user rights" should therefore be wielded carefully if public interest advocates are to avoid self-inflicted injury.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.097 |
| Scholarly communication | 0.024 | 0.048 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".