{"id":"W3048361030","doi":"10.3386/w27649","title":"Assessing the Quality of Illegal Copies and its Impact on Revenues and Distribution","year":2020,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Copyright and Intellectual Property","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Estimation; Revenue; Quality (philosophy); Distribution (mathematics); Econometrics; Statistics; Motion (physics); Computer science; Economics; Mathematics; Artificial intelligence; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003808846,0.0001354746,0.0003491816,0.0001869533,0.0001794357,0.0002815732,0.0001874015,0.000119987,0.0003744134],"category_scores_gemma":[0.003929823,0.00008494179,0.00008727873,0.0001394971,0.0002165729,0.000551325,0.0002236963,0.0003943274,0.00002401629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002417498,"about_ca_system_score_gemma":0.0005128915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002067408,"about_ca_topic_score_gemma":0.00005569183,"domain_scores_codex":[0.9983118,0.0000675677,0.0004542828,0.0002640414,0.0007498875,0.0001524199],"domain_scores_gemma":[0.9973514,0.0008315355,0.0003713985,0.0001083726,0.001323449,0.00001385997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005011056,0.0001305313,0.006119718,0.002824531,0.0004721164,0.000001808877,0.000146608,0.00003382251,0.0009216337,0.1865671,0.7918584,0.01042262],"study_design_scores_gemma":[0.001886406,0.0004460504,0.2833504,0.001933453,0.0001577693,0.0000172777,0.000753223,0.04056105,0.001403301,0.3359199,0.3323722,0.001198969],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3183039,0.002354231,0.000003161545,0.003611164,0.0002970211,0.0006397325,0.0002106185,0.00001552573,0.6745647],"genre_scores_gemma":[0.9974272,0.0005879953,0.000002034941,0.00002780991,0.001022131,0.000009664018,0.00033421,0.0000124817,0.0005764505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6791233,"threshold_uncertainty_score":0.4704649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4716021322664249,"score_gpt":0.5430494252995566,"score_spread":0.07144729303313163,"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."}}