{"id":"W4296591825","doi":"10.1145/3523227.3551476","title":"Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Social Media and Politics","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); Canadian Institute for Advanced Research; McGill University","funders":"European Commission; European Research Council; Canadian Institute for Advanced Research","keywords":"Recommender system; Computer science; Novelty; Citizenship; Cultural diversity; Knowledge management; World Wide Web; Data science; Sociology; Social psychology; Psychology; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00904745,0.0007389403,0.001306178,0.002921688,0.001411692,0.003427444,0.001077603,0.00122231,0.0007987855],"category_scores_gemma":[0.04237683,0.0003823305,0.001054867,0.002488129,0.001343841,0.004536726,0.00353545,0.001585556,0.0002387067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057495,"about_ca_system_score_gemma":0.001076183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005163159,"about_ca_topic_score_gemma":0.005053856,"domain_scores_codex":[0.9921332,0.003802609,0.0006445865,0.001444192,0.001695117,0.0002803933],"domain_scores_gemma":[0.9616143,0.02208663,0.004582958,0.006730669,0.003784056,0.001201396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001309966,0.001532482,0.3984481,0.001050077,0.0020007,0.0004168586,0.01338257,0.05875739,0.02447913,0.0267753,0.002060969,0.4697865],"study_design_scores_gemma":[0.0002115763,0.003851456,0.3746166,0.000502538,0.001531742,0.001305348,0.00884397,0.5044528,0.0255485,0.06351805,0.01492299,0.0006944972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7784898,0.001047306,0.2112786,0.0007110342,0.00005629272,0.0003583953,0.0002633637,0.0003910979,0.007404046],"genre_scores_gemma":[0.9482983,0.0001352197,0.05095875,0.00006668396,0.00002667588,0.00006366766,0.0001138746,0.00001611506,0.0003207595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00904745,"threshold_uncertainty_score":0.04784805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3282805306898309,"score_gpt":0.4205845071152797,"score_spread":0.0923039764254488,"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."}}