{"id":"W3008813561","doi":"","title":"L’impact social des algorithmes de recommandation sur la curation des contenus musicaux francophones au Québec. Enquête qualitative.","year":2019,"lang":"fr","type":"article","venue":"EspaceINRS (National Institute for Scientific Research (Canada))","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","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":[0.02649133,0.0009575005,0.000841839,0.002781708,0.004215692,0.006591064,0.002157151,0.001633259,0.007417798],"category_scores_gemma":[0.1147494,0.0004182004,0.0009355725,0.002823859,0.002528935,0.002885333,0.002162131,0.002140058,0.001308337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0257944,"about_ca_system_score_gemma":0.03494971,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6755407,"about_ca_topic_score_gemma":0.7285708,"domain_scores_codex":[0.9785401,0.01120334,0.0009292024,0.002201388,0.005921197,0.001204786],"domain_scores_gemma":[0.8784488,0.06569508,0.004847337,0.005063538,0.04291061,0.003034603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001607076,0.0009885851,0.1820476,0.005657637,0.0009598368,0.0005733962,0.03173206,0.03204029,0.009765587,0.02018829,0.06461563,0.649824],"study_design_scores_gemma":[0.0007443208,0.00261341,0.3252292,0.006865999,0.002073729,0.00050711,0.06739265,0.1261953,0.01338687,0.01253027,0.4418002,0.0006609333],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.76954,0.011201,0.06116202,0.03171695,0.001018842,0.003431496,0.006302895,0.003287286,0.1123396],"genre_scores_gemma":[0.9080881,0.002204254,0.0612426,0.002041643,0.0001028209,0.001400308,0.003079079,0.0004556833,0.02138544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3244593,"threshold_uncertainty_score":0.6527402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.231747833782327,"score_gpt":0.4046460394048134,"score_spread":0.1728982056224864,"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."}}