{"id":"W4391436413","doi":"10.1145/3643138","title":"Measuring Commonality in Recommendation of Cultural Content to Strengthen Cultural Citizenship","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Recommender Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"University College London; Canadian Institute for Advanced Research","keywords":"Citizenship; Content (measure theory); Cultural diversity; Sociology; Political science; Anthropology; Law","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":[0.01081334,0.0008169144,0.001596573,0.00481455,0.001501959,0.003554344,0.001340539,0.00151219,0.0007741359],"category_scores_gemma":[0.06182242,0.0003046865,0.001180233,0.003837105,0.001202949,0.004345108,0.003200256,0.001597566,0.0003074715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426967,"about_ca_system_score_gemma":0.001294842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008856094,"about_ca_topic_score_gemma":0.008334986,"domain_scores_codex":[0.9908891,0.003480498,0.0008687812,0.001970964,0.002348917,0.000441687],"domain_scores_gemma":[0.9415568,0.03564131,0.005908215,0.009466292,0.005721637,0.001705782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00182588,0.00163968,0.5002716,0.0008797685,0.001487304,0.0002694034,0.004393738,0.07911115,0.01601823,0.0109988,0.00228172,0.3808227],"study_design_scores_gemma":[0.000183006,0.002017741,0.2422292,0.0002037677,0.0006725069,0.0006325961,0.003315041,0.7008606,0.02235848,0.02198867,0.005201523,0.0003367602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8873529,0.0008584207,0.1059797,0.0002897685,0.00004673078,0.0003522979,0.0004664552,0.0005976608,0.004056093],"genre_scores_gemma":[0.9436584,0.0001064967,0.05534476,0.00004894299,0.00001907783,0.00008207728,0.0004042538,0.00002279319,0.0003131251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01081334,"threshold_uncertainty_score":0.05718708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2429439671267938,"score_gpt":0.3216424187380756,"score_spread":0.07869845161128178,"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."}}