{"id":"W2162525194","doi":"10.5204/mcj.220","title":"From TV to Twitter: How Ambient News Became Ambient Journalism","year":2010,"lang":"en","type":"article","venue":"M/C Journal","topic":"Public Relations and Crisis Communication","field":"Social Sciences","cited_by":242,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Citizen journalism; Journalism; Social media; News media; Media studies; China; Quake (natural phenomenon); Technical Journalism; Political science; Sociology; History; Law","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":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001298887,0.0001101498,0.0001562483,0.0001796059,0.001326346,0.001337873,0.0008394082,0.0001292695,0.001649059],"category_scores_gemma":[0.000325962,0.00009587176,0.0001356946,0.0003376293,0.00009412606,0.0005765546,0.0001045312,0.0009419954,0.0001712304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000138567,"about_ca_system_score_gemma":0.0002219813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003424931,"about_ca_topic_score_gemma":0.003650065,"domain_scores_codex":[0.9981918,0.0003028823,0.0002995395,0.0001571385,0.0006781926,0.0003704774],"domain_scores_gemma":[0.9984404,0.00008459707,0.000244627,0.0004126378,0.0002834323,0.0005342491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000541211,0.0005833376,0.03331348,0.000002367402,0.0001798482,0.00004174911,0.1292345,0.00007893075,0.06735053,0.03015896,0.6008056,0.1381966],"study_design_scores_gemma":[0.000254767,0.00003745914,0.0178336,0.00001781832,0.00001764462,0.00001996712,0.008831364,0.00004422266,0.0001845768,0.006389718,0.9662204,0.0001484581],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8341538,0.0001100936,0.004138689,0.1491723,0.001924903,0.000126096,0.000005101871,0.00003358594,0.01033534],"genre_scores_gemma":[0.9899338,0.0002495698,0.003708657,0.001449337,0.002084734,0.00000801506,0.000007303386,0.00001269069,0.002545852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3654148,"threshold_uncertainty_score":0.9999738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0300186511368465,"score_gpt":0.3235255061224732,"score_spread":0.2935068549856267,"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."}}