{"id":"W4400368523","doi":"10.1080/02699206.2024.2374917","title":"Using language sample analyses across English dialects: A case-based approach for preschoolers","year":2024,"lang":"en","type":"article","venue":"Clinical Linguistics & Phonetics","topic":"Language Development and Disorders","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health; University of Minnesota","keywords":"Mean length of utterance; Linguistics; Lexical diversity; Psychology; Morpheme; Syntax; Sentence; Utterance; American English; Language development; Developmental psychology; Vocabulary","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001230405,0.0003076883,0.0004675086,0.0001066719,0.0001887182,0.0002289286,0.0002780493,0.0003685345,0.0002205516],"category_scores_gemma":[0.02364979,0.0002864473,0.0003696054,0.0004280436,0.0002491208,0.00001668867,0.00008312799,0.0004792276,0.00002897548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005161661,"about_ca_system_score_gemma":0.0002260641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002736182,"about_ca_topic_score_gemma":0.00005886881,"domain_scores_codex":[0.9972715,0.0002201485,0.000881495,0.0007537797,0.0002110729,0.0006620385],"domain_scores_gemma":[0.9941107,0.004591382,0.0001344831,0.0005569396,0.0003634079,0.0002430857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008038287,0.01469951,0.07833816,0.007254193,0.01385177,0.02730202,0.3461511,0.04501262,0.0009010541,0.04167967,0.1710747,0.2456969],"study_design_scores_gemma":[0.01585932,0.001866686,0.001698122,0.0005604304,0.004982094,0.0001755517,0.05379709,0.4763781,0.001162426,0.009661889,0.4285495,0.005308734],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1489091,0.00654694,0.8215992,0.00001803687,0.01268894,0.001015103,0.001240485,0.0006225335,0.007359626],"genre_scores_gemma":[0.8329661,0.00001449304,0.1618657,0.0004514674,0.003767582,0.00006123164,0.0004375379,0.00009671277,0.0003391827],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6840571,"threshold_uncertainty_score":0.9999588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2325659836655448,"score_gpt":0.5303198401927516,"score_spread":0.2977538565272068,"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."}}