Bibliographic record
Abstract
In this article, I investigate the nature of exceptions to copyright infringement or users' rights. Are exceptions to copyright infringement rights or privileges? Are they mandatory? While copyright users' rights and interests have triggered interest and debate amongst scholars, relatively less attention has been given to defining their precise nature, and on the consequences of the main characteristics of exceptions to copyright infringement on copyright law and policy. I examine the interplay between the users' rights set out in the Copyright Act and how they can be altered or overridden by non-negotiated standard end-user agreements and TPMs. To this end, I refer to a sample of non-negotiated standard terms of use for the online distribution of books, musical recordings and films. I investigate the nature of exceptions to copyright infringement, including through Hohfeld's theory of jural correlatives. I look at the policy considerations behind these questions and conclude by reflecting on the damaging effects of the uncertain nature of users' rights on the coherence and, ultimately, the legitimacy of copyright law.
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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 | 0.039 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| 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".