{"id":"W2920942353","doi":"10.48550/arxiv.1903.07581","title":"MediaRank: Computational Ranking of Online News Sources","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Popularity; Reputation; Distrust; Ranking (information retrieval); Quality (philosophy); News media; Politics; Rank (graph theory); German; Computer science; Social media; Political science; Internet privacy; Advertising; Public relations; World Wide Web; Information retrieval; Business; History; Mathematics; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003454553,0.0001398055,0.0002629984,0.0002267311,0.0001620798,0.00004613254,0.0005079242,0.0002375528,0.000498462],"category_scores_gemma":[0.0001517206,0.0001586834,0.0001520018,0.0003145702,0.0002372531,0.0002687953,0.0002238508,0.0002797056,0.00008155929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001165598,"about_ca_system_score_gemma":0.000669499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001434019,"about_ca_topic_score_gemma":0.0007559695,"domain_scores_codex":[0.9989582,0.0001481195,0.0002237146,0.0002480061,0.0002060727,0.0002159329],"domain_scores_gemma":[0.9988317,0.0001933612,0.0003917791,0.0002328201,0.0002171385,0.0001331437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006885084,0.00008952979,0.00622348,0.000102784,0.00008696122,0.00000717229,0.02388874,0.8677573,0.000002418549,0.09944517,0.001059578,0.001268068],"study_design_scores_gemma":[0.006184211,0.0002285124,0.04794122,0.00115594,0.0005131413,0.000002936673,0.1120289,0.6234804,0.00006516089,0.1549455,0.05108571,0.002368329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9433032,0.00004355871,0.02060293,0.0003002692,0.0005244026,0.0002822771,0.00007286631,0.0000750826,0.0347954],"genre_scores_gemma":[0.9969141,0.0002303847,0.0002765442,0.0001731027,0.0001240291,3.526907e-8,0.00009653203,0.000007725162,0.002177587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2442768,"threshold_uncertainty_score":0.6470919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101889739550699,"score_gpt":0.2413305733554786,"score_spread":0.1394408338047796,"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."}}