A Window into Generative Artificial Intelligence Under Copyright Law & Policy in Canada
Bibliographic record
Abstract
Generative artificial intelligence alters and challenge existing sociotechnical practices and regulatory schemes. This research provides a window into current the Canadian copyright law and policy context, offering insights derived existing precedent as well as ongoing informal and de facto policymaking processes. Key issues addressed include the copyright implications of text and data mining, training model inputs and outputs, transparency, licensing, and data curation. This work provides insights and guidance on where future policymaking efforts and reforms are most needed. Perspective sur l'intelligence artificielle générative sous la loi et les règlements sur le droit d'auteur au Canada RésuméL'intelligence artificielle générative (IAgen) remet en question, et altère les pratiques socio-techniques et les systèmes de réglementation. Cette étude permet de mettre en perspective le contexte entourant la loi et les politiques sur le droit d'auteur au Canada par rapport à l'IAgen, elle permet de comprendre les antécédents légaux et les initiatives de mise en place de politiques. Les principales problématiques abordées incluent; la nature variée des processus de mise en place de politiques, les implications de l'exploitation de texte et de données (TDM) et des modèles de formation de l'IAgen par rapport à la propriété intellectuelle, les rôles évolutifs de l'accréditation et du stockage des données, puis les considérations éthiques concernant la transparence. Cette étude offre une perspective de l'état actuel de la loi canadienne sur le droit d'auteur à propos de l'IA et offre des conseils quant aux futurs projets de mise en place de politiques et de réformes qui sont le plus importantes. Mots-clésPolitiques; Droit d'auteur; Intelligence artificielle
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".