{"id":"W2952234295","doi":"10.1075/aplv.17001.nag","title":"Classifier use in Heritage and Hong Kong Cantonese","year":2019,"lang":"en","type":"article","venue":"Asia-Pacific Language Variation","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heritage language; Homeland; Noun; Linguistics; Classifier (UML); Psychology; History; Computer science; Artificial intelligence; Political science","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":[0.0008277947,0.0003401551,0.0002588278,0.0009114749,0.001107333,0.001274985,0.0003012611,0.0002770791,0.003154994],"category_scores_gemma":[0.002007139,0.0002061485,0.0001502651,0.001054406,0.0007765982,0.0004794078,0.0006119877,0.000275108,0.0004365254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001302435,"about_ca_system_score_gemma":0.0007052506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2198628,"about_ca_topic_score_gemma":0.3693239,"domain_scores_codex":[0.9995663,0.00009047084,0.0000376519,0.0001139358,0.00009828424,0.00009338285],"domain_scores_gemma":[0.9980986,0.0006067592,0.0004085459,0.0001544718,0.0004889097,0.0002426903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003194423,0.00006528189,0.9188752,0.0000690417,0.00006672213,0.0009749314,0.05314556,0.00009214566,0.009922715,0.00018643,0.0005612454,0.01572134],"study_design_scores_gemma":[0.000004275221,0.00004955761,0.9883779,0.0000114538,0.00001227721,0.0002189443,0.009737154,0.0001581409,0.0006636878,0.00002095558,0.0007322943,0.00001343512],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999377,0.00002384269,0.00001082629,0.000008389836,0.000001097237,0.000002258606,0.00008488227,9.312532e-7,0.0004909352],"genre_scores_gemma":[0.9986516,0.00005396576,0.00005557171,0.00001269079,0.000001089461,0.000005801406,0.0002581306,0.000003246352,0.0009579705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2198628,"threshold_uncertainty_score":0.4371662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03116043850287978,"score_gpt":0.3666992633962405,"score_spread":0.3355388248933607,"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."}}