{"id":"W3155453336","doi":"","title":"A Critical Examination of Chinese Language Media’s Normative Goals and News Decisions","year":2015,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Asian Culture and Media Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normative; Computer science; Linguistics; Artificial intelligence; Political science; Law; Philosophy","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001982422,0.0001548684,0.0005248358,0.0002953779,0.0002649826,0.0003246761,0.0008408701,0.00009370476,0.0007938119],"category_scores_gemma":[0.01481784,0.0001145256,0.00007573039,0.0006869293,0.0005325478,0.002028982,0.0004172151,0.0001871244,0.000007657518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005824321,"about_ca_system_score_gemma":0.0001880778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001736567,"about_ca_topic_score_gemma":0.002696802,"domain_scores_codex":[0.9977499,0.0004982833,0.0004840332,0.0001939788,0.0008306956,0.0002431122],"domain_scores_gemma":[0.9968827,0.001588933,0.0003370791,0.0001572055,0.0006549181,0.0003791382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001267698,0.0003831756,0.3954369,0.0000555586,0.0001725307,0.00005519805,0.4115634,0.000003773115,0.005304618,0.002437194,0.06295784,0.1215031],"study_design_scores_gemma":[0.0006901851,0.00001890583,0.8771558,0.0003125368,0.00008673574,0.000004718929,0.09225855,0.000007822256,0.001127464,0.01923764,0.008791935,0.0003077248],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8941594,0.02025283,0.0002476972,0.001192802,0.0005911482,0.0003629579,0.00004688401,0.00002178627,0.0831245],"genre_scores_gemma":[0.9920994,0.006779411,0.0004263818,0.0001420661,0.0002832708,0.00001802778,0.000004675584,0.00001084739,0.0002359347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4817189,"threshold_uncertainty_score":0.9934807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.292209163849264,"score_gpt":0.6012879202944328,"score_spread":0.3090787564451687,"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."}}