{"id":"W4402373012","doi":"10.4324/9781003465645","title":"Decoding Korean Political Talk","year":2024,"lang":"en","type":"book","venue":"","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prompt (Canada)","funders":"","keywords":"Decoding methods; Politics; Political science; Computer science; History; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006131752,0.0006097708,0.0001563065,0.001060822,0.002241106,0.004881015,0.0003931758,0.0005884775,0.01782511],"category_scores_gemma":[0.001652622,0.0002107116,0.0001647761,0.001638016,0.002145676,0.005438293,0.002230244,0.001571365,0.004200245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002056165,"about_ca_system_score_gemma":0.001505777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00262027,"about_ca_topic_score_gemma":0.008590207,"domain_scores_codex":[0.9995896,0.0001626255,0.00002266239,0.00005785025,0.00009333526,0.00007398701],"domain_scores_gemma":[0.9995105,0.000205926,0.00004545983,0.00005255639,0.0001420927,0.00004327304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001114241,0.00002074015,0.002185818,0.000681152,0.00001376854,0.0009261057,0.2073785,0.0003107946,0.004624544,0.4554771,0.167172,0.161098],"study_design_scores_gemma":[0.00000354138,0.00000895523,0.001618697,0.0001962187,0.000007607086,0.0003019381,0.05662366,0.0003000874,0.001131634,0.007036613,0.9327561,0.00001491247],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0848112,0.008009357,0.00927127,0.01291459,0.002996625,0.00008012543,0.001013006,0.0002910036,0.8806129],"genre_scores_gemma":[0.5987756,0.007202777,0.005476695,0.003062905,0.0004254186,0.0001153696,0.00145783,0.001084825,0.3823986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01782511,"threshold_uncertainty_score":0.05963087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04301162688854709,"score_gpt":0.3570648403723681,"score_spread":0.314053213483821,"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."}}