Non-commercial user-generated content and exceptions to copyright : a comparison between the US, Canada, and the EU
Bibliographic record
Abstract
The topic of this thesis is how existing limitations and exceptions to copyright address the conflict between the economic interests of rightsholders and the interests of users in producing and disseminating user-generated content. Creative appropriation is heavily present in user-generated content in the Web 2.0. However, appropriative expressions often clash with copyright. Technological developments have resulted in the expansion of exclusive rights at the expense of limitations and exceptions. The concerns of rightsholders are increasingly focused on protecting economic interests. The thesis utilizes the legal dogmatic approach and the comparative legal method with a focus on the US, Canadian, and EU jurisdictions. Microcomparative comparison between the jurisdictions examines how each of them have addressed the balancing of interests between rightsholders and users in their limitations and exceptions. The political and historical contexts of the jurisdictions are discussed on a macrocomparative level. The main sources are constitutional authorities, statutory copyright legislation and case-law of each of the jurisdictions. References are also made to international treaties, such as Berne, TRIPS, WCT and WPPT. The main findings of the thesis are that US fair use accepts a wide range of purposes – many of which pertain to user-generated content. However, the ambiguous nature of the doctrine may be a hurdle for users. In Canadian copyright law, user-generated content benefits from a fair dealing exception and a non-commercial UGC exception, which are to be interpreted widely and without unduly limiting users’ rights. EU copyright law recognizes the need to balance between the interests of rightsholders and users, but limitations and exceptions are interpreted strictly and regarded as derogations to exclusive rights. The approach in EU lacks the mechanisms and flexibilities to effectively respond to and protect the new user-generated types of expressions prominent in the Web 2.0. The thesis concludes that an imperative step for the EU is to elevate limitations and exceptions to copyright to the same level as exclusive rights. Additionally, a combination of a specific exception for non-commercial user-generated content and a (semi-)open-ended exception allows for effective balancing between rightsholders’ and users’ interests. A wider-scale examination of the different aspects of copyright law that impact user-generated content is required in order to better understand the effects and normative potential of the exceptions analyzed in this thesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".