Control and Compensation. A Comparative Analysis of Copyright Exceptions for Training Generative AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lawmakers and administrative agencies around the globe are debating whether the use of copyrighted content for AI training does or should require the rights holder’s consent. This article examines legislation and policy debates in the U.S., Canada, the UK, the EU, Israel, China, Singapore, and Japan. Issues of control, compensation, transparency and legal certainty dominate the discussion. Countries are trying to recalibrate the balance of interests, either in favour of AI companies, or by supporting rights holders – for example, with copyright-related transparency obligations. EU copyright law offers a relatively favourable environment for AI companies. Ultimately, however, copyright law is not the decisive factor when AI companies choose the location of their training facilities.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| 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 it