{"id":"W2929826240","doi":"10.1017/s1366728919000014","title":"Triggered codeswitching: Lexical processing and conversational dynamics","year":2019,"lang":"en","type":"article","venue":"Bilingualism Language and Cognition","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Max Planck Instituut voor Psycholinguïstiek; British Academy","keywords":"Context (archaeology); Linguistics; Computer science; Lexical item; Speech production; Psychology; Welsh; Natural language processing; Speech recognition; Biology","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":[0.0004195461,0.0001507861,0.0001625824,0.0003351089,0.000322526,0.0008630037,0.0002107655,0.0002574842,0.001715372],"category_scores_gemma":[0.004835613,0.0001824224,0.0001304093,0.0002047387,0.0006415473,0.0009619697,0.0007547231,0.0003006525,0.0001623915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003087193,"about_ca_system_score_gemma":0.0002604801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000828177,"about_ca_topic_score_gemma":0.000985403,"domain_scores_codex":[0.9996359,0.0001286101,0.00001771905,0.00009593822,0.00008365526,0.00003806359],"domain_scores_gemma":[0.9976079,0.001536335,0.0003610163,0.0001876296,0.0001871259,0.000119947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007982305,0.00011971,0.02504763,0.0002619661,0.00004331942,0.001482347,0.009991604,0.0006998658,0.9002483,0.008768339,0.0002095065,0.05232913],"study_design_scores_gemma":[0.00009127953,0.0007205881,0.6680524,0.0001179538,0.0001172985,0.00280612,0.008917444,0.02552065,0.2538891,0.0325456,0.007057459,0.0001640971],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874814,0.0001331386,0.007042009,0.00006216389,0.000009601516,0.00002602675,0.00006028424,0.00005441311,0.005130889],"genre_scores_gemma":[0.9979936,0.00003965226,0.001592878,0.00001261395,0.000005998093,0.00001174567,0.00002884859,0.00001274074,0.0003020299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001715372,"threshold_uncertainty_score":0.005738437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174203726603213,"score_gpt":0.2733394122516737,"score_spread":0.2559190395913524,"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."}}