{"id":"W4410705340","doi":"10.29173/cais1951","title":"A Window into Generative Artificial Intelligence Under Copyright Law & Policy in Canada","year":2025,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Generative grammar; Window (computing); Copyright law; Law; Artificial intelligence; Law and economics; Political science; Computer science; Sociology; Intellectual property; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000770576,0.000536591,0.0008802564,0.000325471,0.0003436072,0.003370925,0.004000076,0.0002879347,0.0001061163],"category_scores_gemma":[0.01097701,0.0004281441,0.0002155328,0.00203447,0.001906269,0.008897401,0.001629602,0.0007164503,0.00001148094],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104711,"about_ca_system_score_gemma":0.00852412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7580652,"about_ca_topic_score_gemma":0.6230542,"domain_scores_codex":[0.9962317,0.0001241695,0.00121078,0.0007783973,0.0007337795,0.000921122],"domain_scores_gemma":[0.9673512,0.0004134082,0.0007163892,0.0004717544,0.03080212,0.0002451366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001637163,0.0002490005,0.003271924,0.0004690267,0.0001497847,0.000003424489,0.05237272,0.0001419093,0.008023646,0.9071063,0.008684629,0.01936397],"study_design_scores_gemma":[0.0004677557,0.0004379909,0.003598948,0.002044149,0.0001173676,0.0000433175,0.00587723,0.02348389,0.3059409,0.4623699,0.1945599,0.00105871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7525488,0.003882935,0.02012177,0.1220897,0.003297987,0.001765937,0.0003117273,0.00008588615,0.09589533],"genre_scores_gemma":[0.9897496,0.0003384302,0.00165918,0.003337885,0.0002199047,0.00003270724,0.000002687758,0.00002349833,0.004636154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4447364,"threshold_uncertainty_score":0.999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04178817317128835,"score_gpt":0.2701740145895669,"score_spread":0.2283858414182786,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}