Paying it Forward: The Case for a Specific Statutory Limitation on Exclusive Rights for User-Generated Content Under Copyright Law
Bibliographic record
Abstract
This article examines user-generated content (“UGC”) and the significance of re-inventions in the context of an increasingly user-centric internet environment and an information sharing society. It will explain the need to provide a statutory limitation in the form of an exception or exemption for socially beneficial UGC on the exclusive rights under copyright law. This will also have the effect of protecting the internet intermediary that hosts and shares UGC. Nascent but abortive attempts have been made by Canada to introduce just such a provision into her copyright legislation, while some principles and rules have also emerged from various interest groups and stakeholders in the attempt of providing a balanced approach towards UGC under the larger scheme of copyright objectives. Customary internet usages and norms relating to UGC will also be examined. These will be evaluated with a view to extracting useful guidelines to construct the parameters of a fair statutory limitation proposed for the legal reform of copyright law. Copyright © 2011 The John Marshall Law School Cite as Warren B. Chik, Paying it Forward: The Case for a Specific Statutory Limitation on Exclusive Rights for User-Generated Content Under Copyright Law, 11 J. MARSHALL REV. INTELL. PROP. L. 240 (2011).
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.027 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".