{"id":"W7116633645","doi":"10.30574/ijsra.2023.10.1.0723","title":"Rebalancing Rights: how Generative AI forces a rethink of fair use/ fair dealing under Canadian and USA law","year":2023,"lang":"","type":"article","venue":"International Journal of Science and Research Archive","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fair use; Generative grammar; Flexibility (engineering); Doctrine; Jurisdiction; Statutory law; Statutory interpretation; Fair dealing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01905021,0.0004514647,0.0005699657,0.003320987,0.02544004,0.02429039,0.00346671,0.006414534,0.006365681],"category_scores_gemma":[0.03681258,0.0003912549,0.0008295505,0.003154856,0.06628699,0.01175641,0.008157672,0.01015008,0.0004634031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09711146,"about_ca_system_score_gemma":0.1524775,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9178718,"about_ca_topic_score_gemma":0.9221293,"domain_scores_codex":[0.9820725,0.00404008,0.0003942771,0.001922335,0.007555951,0.004014807],"domain_scores_gemma":[0.9823259,0.007512466,0.0006291872,0.002656185,0.005408709,0.001467559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005529064,0.000006032621,0.0003469984,0.000005944715,0.000002471201,0.00005388119,0.006860794,0.0002751726,0.00007173434,0.9836618,0.002360536,0.006349265],"study_design_scores_gemma":[0.00003316076,0.00002548894,0.003024993,0.000365,0.00004539348,0.0001655445,0.01802224,0.004706118,0.0008595209,0.6790626,0.2935544,0.0001355047],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08394297,0.002440773,0.05141448,0.1229634,0.0006996185,0.0001586635,0.0001432417,0.0002828885,0.737954],"genre_scores_gemma":[0.9514516,0.0007910348,0.009682016,0.007574796,0.0001202433,0.00005661747,0.00004635058,0.0001309488,0.03014637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09711146,"threshold_uncertainty_score":0.7045963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08546434935875986,"score_gpt":0.3510882664031826,"score_spread":0.2656239170444227,"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."}}