{"id":"W1618975677","doi":"","title":"Code-Switching in Persian/English and Korean/English Conversations: with a focus on light verb constructions","year":2009,"lang":"en","type":"article","venue":"","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Code-switching; Verb; Linguistics; Inflection; Adverb; Noun; Computer science; Natural language processing; Word order; Causative; Persian; Object (grammar); Context (archaeology); Artificial intelligence; History","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001483107,0.0002987387,0.0002653699,0.0009768012,0.001699221,0.001480654,0.0003039756,0.0004405112,0.0008208585],"category_scores_gemma":[0.008771029,0.0002330405,0.0001589354,0.0007358253,0.001505949,0.0009071213,0.001612095,0.0007465908,0.0001471753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00115578,"about_ca_system_score_gemma":0.001098217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0216256,"about_ca_topic_score_gemma":0.04282595,"domain_scores_codex":[0.9987048,0.0006005666,0.00008162422,0.0001953583,0.0002253558,0.0001921887],"domain_scores_gemma":[0.9950022,0.002872164,0.000792935,0.0002267729,0.0006569601,0.0004489085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003044124,0.00008431581,0.08717701,0.0001242159,0.00002011803,0.0006005015,0.8737426,0.00003906232,0.02311182,0.0005511752,0.000231601,0.01401315],"study_design_scores_gemma":[0.00002834582,0.00012156,0.2355635,0.00006772078,0.00002869741,0.001136371,0.7494629,0.0004651907,0.006639916,0.0004409477,0.005964566,0.00008025347],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986512,0.00005949312,0.0001700627,0.00003738105,0.000005236785,0.0000137191,0.00002861268,0.000003098299,0.0010312],"genre_scores_gemma":[0.9989551,0.00008756998,0.0003265339,0.00007307695,0.000005319861,0.00002537274,0.00009031953,0.000009325854,0.0004274496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0216256,"threshold_uncertainty_score":0.04299945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02296710938191069,"score_gpt":0.3548693799594546,"score_spread":0.3319022705775439,"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."}}