{"id":"W2002561419","doi":"10.1080/15434303.2014.936603","title":"Using Lexical Profiling Tools to Investigate Children’s Written Vocabulary in Grade 3: An Exploratory Study","year":2015,"lang":"en","type":"article","venue":"Language Assessment Quarterly","topic":"Writing and Handwriting Education","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Calgary","funders":"","keywords":"Vocabulary; Rubric; Lexical diversity; Salient; Psychology; Computer science; Linguistics; Profiling (computer programming); Exploratory research; Vocabulary development; Natural language processing; Lexical density; Trait; Artificial intelligence; Mathematics education; Lexical item; Sociology","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":[],"consensus_categories":[],"category_scores_codex":[0.002637984,0.000147985,0.0002077686,0.0001803436,0.0002353262,0.0003643282,0.0002291485,0.00007915723,0.00001125822],"category_scores_gemma":[0.0001356636,0.0001580007,0.00003082362,0.0003914221,0.00006112197,0.0007167904,0.0000220987,0.0002436164,0.00001509819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002959958,"about_ca_system_score_gemma":0.0005688856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006299836,"about_ca_topic_score_gemma":0.01298977,"domain_scores_codex":[0.9972938,0.001050411,0.0003125855,0.000384343,0.0005367931,0.0004220735],"domain_scores_gemma":[0.9991072,0.00006832943,0.00008037413,0.0002718226,0.00007916812,0.0003931522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000125865,0.001161682,0.43023,0.000008751249,0.00001833998,0.00007879752,0.5546758,0.00007963276,0.003168103,0.001595917,0.0001058807,0.00886455],"study_design_scores_gemma":[0.0007641609,0.0007511763,0.1161382,0.00007210406,0.00002328103,0.000003671978,0.8810411,0.0003250043,0.0001297832,0.0003811876,0.00003249125,0.0003378797],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970941,0.00004538858,0.0002869081,0.0005533728,0.0002307511,0.0007520586,0.000005741676,0.000157382,0.0008743054],"genre_scores_gemma":[0.9917354,3.749739e-7,0.00706238,0.0003061135,0.000696584,0.00008802666,0.0000349129,0.0000223749,0.00005383479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3263653,"threshold_uncertainty_score":0.952351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1277240996145899,"score_gpt":0.430229454825071,"score_spread":0.3025053552104811,"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."}}