{"id":"W1984948218","doi":"10.1145/2209310.2209311","title":"A Computational Framework for Media Bias Mitigation","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Media Influence and Politics","field":"Social Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; Ministry of Knowledge Economy","keywords":"Media bias; Viewpoints; Computer science; Journalism; Social media; Event (particle physics); Polarization (electrochemistry); Internet privacy; News media; Mass media; The Internet; Credibility; Data science; Politics; Political science; World Wide Web; Law","routes":{"ca_aff":true,"ca_fund":false,"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":[0.006818673,0.0009683887,0.00125363,0.001971994,0.001730948,0.003629509,0.003268652,0.002689154,0.01012142],"category_scores_gemma":[0.03175452,0.0007383455,0.001472959,0.001974438,0.003287978,0.005753317,0.004759281,0.002703667,0.0009816259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415964,"about_ca_system_score_gemma":0.003009861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004131746,"about_ca_topic_score_gemma":0.004125165,"domain_scores_codex":[0.9952955,0.002936882,0.0001483761,0.000602619,0.0007176542,0.0002989395],"domain_scores_gemma":[0.9786805,0.01753836,0.0009569199,0.001433937,0.0009733714,0.0004170117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001245129,0.00009616079,0.001386779,0.0001702201,0.00008865983,0.0001505776,0.0002958759,0.3168116,0.0006690987,0.6349583,0.00669393,0.03855433],"study_design_scores_gemma":[0.00003525766,0.00001685297,0.0001153999,0.00002056183,0.00001421292,0.0000351986,0.00004866653,0.7262553,0.000194232,0.2696322,0.003618956,0.00001319532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006484562,0.0002207064,0.9830902,0.001575106,0.0001160745,0.00007662499,0.0002030801,0.0002923483,0.007941358],"genre_scores_gemma":[0.4343791,0.0005665929,0.5537929,0.0008277786,0.0007622647,0.0006616143,0.0006375867,0.0002747373,0.008097468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01012142,"threshold_uncertainty_score":0.03606099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124844921086156,"score_gpt":0.3972061025637309,"score_spread":0.2847216104551153,"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."}}