{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001571589,0.0002235566,0.0005642847,0.000113841,0.0003214489,0.0001291445,0.0005281523,0.0002772485,0.0007667404],"category_scores_gemma":[0.001158963,0.000218486,0.0001580982,0.0003100013,0.0001437383,0.0001037801,0.0004511518,0.0007565389,0.00001185822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0011167,"about_ca_system_score_gemma":0.0002430709,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08367949,"about_ca_topic_score_gemma":0.008456154,"domain_scores_codex":[0.9962024,0.001654255,0.0007365427,0.0003905873,0.000534237,0.0004819844],"domain_scores_gemma":[0.9982927,0.0005450727,0.0003511483,0.0002312488,0.0003705323,0.0002092299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00019614,0.0003937355,0.1862011,0.001138722,0.0004115239,0.000007052113,0.6231851,0.0005818747,0.0005949568,0.1188582,0.05795454,0.01047702],"study_design_scores_gemma":[0.0005639714,0.00008195498,0.01170426,0.000603082,0.00007425309,9.840044e-7,0.8671807,0.00007555581,0.001279265,0.00706903,0.1101951,0.001171815],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8968279,0.0001647874,0.00008032815,0.01043114,0.005755296,0.00130858,0.0002035611,0.0001083018,0.08512016],"genre_scores_gemma":[0.9966142,0.0001407727,0.0001997022,0.0004684733,0.0005132584,0.0002639459,0.0002248541,0.00001494416,0.00155989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2439956,"threshold_uncertainty_score":0.9224223,"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."}}