{"id":"W4391618161","doi":"10.32920/25175192.v1","title":"News Personalization: Do Journalism Audiences Prefer Algorithms Over Editors?","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University; Centre for Social Innovation; York University","funders":"","keywords":"Personalization; Journalism; Preference; Variety (cybernetics); Computer science; World Wide Web; Social media; Democracy; Algorithm; Political science; Advertising; Business; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004931529,0.0003529584,0.0003922529,0.0003518602,0.0001630327,0.002679333,0.002201056,0.000263286,0.0006720854],"category_scores_gemma":[0.00005378326,0.0002642414,0.0003335012,0.000622833,0.00006263839,0.0004095963,0.003981488,0.0007739144,0.0003295945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008102188,"about_ca_system_score_gemma":0.0003429009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003689844,"about_ca_topic_score_gemma":0.00002973106,"domain_scores_codex":[0.9970479,0.00009794345,0.0004477927,0.001156314,0.0009081275,0.0003419218],"domain_scores_gemma":[0.9982746,0.00006887371,0.0002041915,0.001098468,0.0001691932,0.0001846357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001286869,0.00006001159,0.0004089121,0.0001818182,0.0004307473,0.00008448085,0.003032165,0.0006363682,0.00001419644,0.02330953,0.9344242,0.03741625],"study_design_scores_gemma":[0.0002223097,0.00006254311,0.0005107783,0.0009097386,0.0003165772,0.00005627311,0.0004385819,0.5379485,0.00009990222,0.05192793,0.406161,0.001345761],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00163322,0.004618678,0.9106197,0.01296674,0.03315919,0.0002227252,0.0001048948,0.0009403595,0.03573447],"genre_scores_gemma":[0.1988159,0.00397084,0.5206628,0.005130922,0.1277654,0.0002604847,0.0005876657,0.0002357118,0.1425703],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5373122,"threshold_uncertainty_score":0.999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530754122415092,"score_gpt":0.2875910575353244,"score_spread":0.2622835163111735,"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."}}