{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01531003,0.0003956678,0.0002120711,0.00457994,0.01335969,0.008862614,0.00115121,0.0007640815,0.001897449],"category_scores_gemma":[0.02339243,0.0002439805,0.0002416416,0.004322652,0.0139263,0.004333044,0.00275353,0.002181269,0.0001167764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.029488,"about_ca_system_score_gemma":0.02808732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3141574,"about_ca_topic_score_gemma":0.411389,"domain_scores_codex":[0.9935461,0.002686154,0.000264824,0.0004136811,0.001859365,0.001229885],"domain_scores_gemma":[0.9741479,0.01468284,0.001448713,0.0009946617,0.007484701,0.001241248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00005419929,0.00004123162,0.02276403,0.00007878456,0.00001072124,0.0005057784,0.8423309,0.00007307973,0.0007400728,0.1138709,0.002991585,0.01653877],"study_design_scores_gemma":[0.00000411177,0.000016116,0.03144575,0.0001032887,0.00001227021,0.00006045019,0.9320795,0.0002791414,0.0007687741,0.005794385,0.02940879,0.00002742794],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8186746,0.0004886717,0.001127998,0.008612147,0.0001362061,0.000124527,0.0001479643,0.00001308468,0.1706749],"genre_scores_gemma":[0.995249,0.0002206258,0.000348816,0.00039138,0.00002470695,0.00004435832,0.00004618354,0.000008959392,0.003665912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3141574,"threshold_uncertainty_score":0.6246576,"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."}}