Comparative Analysis of U.S. and Canadian Approaches to Copyright Policy in the Age of AI
Bibliographic record
Abstract
As the integration of artificial intelligence (AI) in everyday life (and particularly in education) increases in what seems to be an exponential way, lawmakers are racing to catch up with policy implications. This poster will present the results of an analysis of cases, legislation, and literature (widely defined) related to copyright concerns involved in the creation and use of AI. The review will take the form of a comparative analysis of approaches of the United States and Canada in crafting policy to address the incorporation of copyrighted materials in training generative AI systems such as ChatGPT and Midjourney and the use of such output in various settings such as education. The analysis will consider existing copyright laws (including user rights such as fair use/dealing and educational uses), and proposed changes to the current laws.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.034 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".