{"id":"W2901523707","doi":"10.31470/2309-1797-2018-24-2-254-276","title":"Mixing and switching of speech codes of Ukrainian emigration (on the example of memoirs and epistolary works by Ulas Samchuk)","year":2018,"lang":"en","type":"article","venue":"PSYCHOLINGUISTICS","topic":"Linguistics, Language Diversity, and Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Code-mixing; Code-switching; Mixing (physics); Computer science; Linguistics; Subject (documents); Lexicalization; Sentence; Natural language processing; Physics","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":[0.001446684,0.0004315691,0.000281887,0.001198619,0.005217259,0.002300724,0.000532035,0.001119338,0.002800077],"category_scores_gemma":[0.00314049,0.0001839224,0.0002159846,0.001058235,0.006901759,0.00236427,0.002587168,0.001665581,0.000619042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002952888,"about_ca_system_score_gemma":0.000961109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01377687,"about_ca_topic_score_gemma":0.01428308,"domain_scores_codex":[0.9988567,0.0006607134,0.00004522493,0.000164633,0.0001133323,0.0001593972],"domain_scores_gemma":[0.9993502,0.0003474579,0.0001019466,0.00008416767,0.00006937621,0.00004682475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003823844,0.000041126,0.00931765,0.000447762,0.0000757909,0.00342124,0.521189,0.0007850218,0.00192537,0.3431401,0.02435433,0.09492026],"study_design_scores_gemma":[0.00002807607,0.000147952,0.06765245,0.0006953391,0.00004620462,0.003802864,0.170398,0.001103084,0.003635396,0.05401454,0.6983441,0.0001319983],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.748834,0.02537789,0.009572298,0.01360249,0.001907925,0.00005740178,0.0005550227,0.0002342333,0.1998586],"genre_scores_gemma":[0.9844509,0.002048611,0.0008309335,0.000474308,0.0001274718,0.00001356475,0.0001254597,0.00006652601,0.01186203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01377687,"threshold_uncertainty_score":0.02739334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03561102156334211,"score_gpt":0.2536817167785762,"score_spread":0.2180706952152341,"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."}}