{"id":"W2651061313","doi":"10.5539/ass.v13n7p142","title":"Precedent Names in the Text Field of Marina Tsvetaeva from the Perspective of Free Indirect Discourse","year":2017,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Discourse Analysis and Cultural Communication","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Persona; Perspective (graphical); Theme (computing); Character (mathematics); Field (mathematics); Linguistics; Function (biology); Poetry; Perception; Literature; Philosophy; Computer science; Art; Epistemology; Artificial intelligence; Humanities; World Wide Web; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001715846,0.00005981907,0.0001378926,0.00002415459,0.001993159,0.0002106855,0.00348755,0.00004213859,0.00008590813],"category_scores_gemma":[0.001171002,0.000033323,0.00009684399,0.0003707191,0.002591056,0.0004475437,0.0002596455,0.0001345917,0.000002537232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000662078,"about_ca_system_score_gemma":0.0001862059,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04062505,"about_ca_topic_score_gemma":0.07117765,"domain_scores_codex":[0.9985693,0.000272098,0.0001529091,0.0001576783,0.0006705811,0.0001774159],"domain_scores_gemma":[0.9987346,0.0001691331,0.0003100106,0.0006224646,0.0001392859,0.00002450403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00001092887,0.0001116591,0.02213614,0.000001157291,0.00002329875,5.588597e-7,0.4338029,1.823728e-7,0.0003814434,0.4363356,0.0007632468,0.1064329],"study_design_scores_gemma":[0.00007162802,0.00001833836,0.5566712,0.00001657048,0.00002434132,4.566314e-8,0.4112052,0.000002216164,0.0002827753,0.03110874,0.0005469669,0.00005199767],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3094346,0.0001050645,0.000003515959,0.04293415,0.00005919157,0.0001451063,0.000007454738,0.000003638012,0.6473072],"genre_scores_gemma":[0.9995031,0.00007426651,0.00002895644,0.00008653732,0.0001059908,0.00000756597,6.643269e-7,0.000001600665,0.0001913435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6900684,"threshold_uncertainty_score":0.9993061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03295008645086032,"score_gpt":0.391777375595382,"score_spread":0.3588272891445217,"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."}}