{"id":"W4391617684","doi":"10.32920/25175192","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; Computer science; Variety (cybernetics); Social media; World Wide Web; Algorithm; Advertising; Business; Artificial intelligence; Mathematics","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":[0.008304941,0.0002483707,0.0003722909,0.00133545,0.001179747,0.004851458,0.0003252717,0.001064502,0.007656851],"category_scores_gemma":[0.0436176,0.0002660555,0.0003681205,0.001026778,0.0009072138,0.004115138,0.000879994,0.001049794,0.0010435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004905089,"about_ca_system_score_gemma":0.0002607823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001232963,"about_ca_topic_score_gemma":0.001467638,"domain_scores_codex":[0.996182,0.002029937,0.0002749009,0.0005433498,0.0006681615,0.0003017313],"domain_scores_gemma":[0.9661437,0.02143308,0.006638219,0.001450145,0.002993722,0.001341127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001952563,0.0008162356,0.6923594,0.000657302,0.0003586968,0.0002435597,0.0379796,0.000558745,0.007697691,0.00641533,0.005540031,0.2454209],"study_design_scores_gemma":[0.0002172105,0.001311657,0.9017732,0.0002331618,0.0004101553,0.0005002758,0.05748162,0.005918486,0.005495799,0.008694999,0.01783433,0.0001291701],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702578,0.0003448326,0.005217852,0.002011919,0.00006878194,0.00009004087,0.0001477399,0.00006101552,0.02179999],"genre_scores_gemma":[0.9967232,0.0001660409,0.001442586,0.0003721643,0.00007361152,0.00003966922,0.00007306813,0.00001370207,0.001095912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008304941,"threshold_uncertainty_score":0.04392123,"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."}}