{"id":"W2066612308","doi":"10.1155/2014/387637","title":"Does Mother Know Best? Maternal Knowledge Calibration Predicts Children’s Oral Language Development","year":2014,"lang":"en","type":"article","venue":"Child Development Research","topic":"Language Development and Disorders","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Institute of Education Sciences","keywords":"Certainty; Psychology; Comprehension; Developmental psychology; Domain knowledge; Vocabulary; Active listening; Privilege (computing); Knowledge level; Listening comprehension; Calibration; Language development; General knowledge; Social psychology; Communication; Computer science; Mathematics education; Linguistics; Artificial intelligence; Statistics","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":[0.001667521,0.00020972,0.0002518671,0.0005659743,0.0003866986,0.001315075,0.0003730923,0.0006081136,0.002438121],"category_scores_gemma":[0.01548193,0.0003414954,0.0002933933,0.0003601673,0.0004955978,0.0006776045,0.0007341639,0.0006689245,0.0003984369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004866906,"about_ca_system_score_gemma":0.0006114746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008651602,"about_ca_topic_score_gemma":0.009826575,"domain_scores_codex":[0.9992531,0.0002558058,0.00007604952,0.00009931497,0.0002074034,0.0001083864],"domain_scores_gemma":[0.9903645,0.004453048,0.003591007,0.000497986,0.0005472314,0.0005462264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005426531,0.00007026579,0.9914767,0.00001544053,0.00002080096,0.0001592643,0.003141533,0.00003925992,0.0003564449,0.00006669115,0.0001009688,0.004498323],"study_design_scores_gemma":[0.000002374975,0.0001323419,0.9945219,0.00005344146,0.0000342882,0.0003068435,0.00377177,0.0002214487,0.0004936994,0.000124199,0.0003291985,0.000008468837],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991481,0.0001820574,0.00003345507,0.00009864571,0.000003060356,0.00000181187,0.00004874151,0.000002131617,0.0004819684],"genre_scores_gemma":[0.9992115,0.0003147355,0.0001201554,0.00001645426,0.000002695875,0.000005667783,0.00006431981,0.000001673672,0.0002626021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008651602,"threshold_uncertainty_score":0.0172025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02533559582971923,"score_gpt":0.350005987921319,"score_spread":0.3246703920915998,"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."}}