{"id":"W4210873251","doi":"10.1017/s0008197321001045","title":"REMARKS ON TECHNOLOGICAL NEUTRALITY IN COPYRIGHT LAW AS A SUBJECT MATTER PROBLEM: LESSONS FROM CANADA","year":2022,"lang":"en","type":"article","venue":"The Cambridge Law Journal","topic":"Copyright and Intellectual Property","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Neutrality; Copyright law; Proposition; Subject matter; Expression (computer science); Subject (documents); Fair use; Net neutrality; Law and economics; Law; Liability; Intellectual property; Corollary; Fair dealing; Nothing; Digital Millennium Copyright Act; Political science; Sociology; Computer science; The Internet; Epistemology; Philosophy; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009282916,0.0003799688,0.001398755,0.002544846,0.01754158,0.01665128,0.003496387,0.00969173,0.01615155],"category_scores_gemma":[0.0340805,0.0003888775,0.0009083932,0.004091084,0.02792438,0.01234442,0.005390506,0.009899064,0.0008096556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07579231,"about_ca_system_score_gemma":0.1001198,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9418126,"about_ca_topic_score_gemma":0.8964829,"domain_scores_codex":[0.9903691,0.001637692,0.0004041921,0.001200977,0.003742272,0.002645625],"domain_scores_gemma":[0.9793783,0.01074788,0.0005849263,0.001441422,0.006382384,0.001465108],"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":[0.00001522183,0.00001115496,0.000374082,0.00001930728,0.000006686707,0.0001237265,0.000850302,0.0004534518,0.00005404998,0.9898317,0.004381812,0.00387841],"study_design_scores_gemma":[0.00005675373,0.00001492231,0.001338583,0.0001658621,0.00002740887,0.00008228509,0.002724479,0.002314925,0.0002689975,0.8900715,0.1028623,0.00007193438],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05223403,0.004302358,0.009399248,0.2394668,0.0005768592,0.00008688631,0.0003118501,0.0001184504,0.6935035],"genre_scores_gemma":[0.9132167,0.003509778,0.004503967,0.02228229,0.0004669944,0.0000780222,0.0001236227,0.0001316845,0.05568688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07579231,"threshold_uncertainty_score":0.5499143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979830643579752,"score_gpt":0.2230356688341039,"score_spread":0.2032373623983064,"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."}}