{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002850315,0.0007036569,0.0008918109,0.002811108,0.001272065,0.002369019,0.0007815477,0.0007717659,0.0009342774],"category_scores_gemma":[0.007339039,0.0004934218,0.0006881803,0.001456923,0.0009905923,0.001120888,0.001743506,0.0009924689,0.0005055233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008771924,"about_ca_system_score_gemma":0.001079227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01331392,"about_ca_topic_score_gemma":0.02763288,"domain_scores_codex":[0.9982528,0.0003965021,0.000264854,0.0002717743,0.0004599855,0.0003540693],"domain_scores_gemma":[0.995279,0.001518429,0.001186627,0.0003573123,0.001179117,0.0004796107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001436547,0.0008237435,0.8325698,0.0001270803,0.00003999272,0.001497062,0.1294196,0.0001112468,0.008976339,0.0001639265,0.0001761132,0.02595139],"study_design_scores_gemma":[0.000008310734,0.0009486577,0.9523659,0.0000432082,0.00002849524,0.0008380734,0.0422304,0.000144015,0.002025608,0.00008787864,0.001257081,0.00002238704],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995182,0.00002386856,0.00007612131,0.000005214041,7.02961e-7,0.00002028983,0.00004481736,0.000003337695,0.0003075753],"genre_scores_gemma":[0.9985531,0.00007456695,0.0006464273,0.00001641259,0.000001543682,0.00009305344,0.0001154383,0.000005291793,0.0004942061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01331392,"threshold_uncertainty_score":0.02647287,"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."}}