{"id":"W4361281563","doi":"10.31730/osf.io/cmb7x","title":"Individual Differences in Leveraging Regularity in Emergent L2 Readers in Rural Côte d’Ivoire","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Orthography; Reading (process); Psychology; Literacy; Consistency (knowledge bases); Linguistics; Leverage (statistics); Contrast (vision); Cognitive psychology; Computer science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001535061,0.0002972563,0.0004714038,0.0007050622,0.00004919473,0.0002380824,0.001812141,0.0003616812,0.00002133031],"category_scores_gemma":[0.0001376862,0.0002904977,0.00009078481,0.0008719955,0.0000769895,0.0003300072,0.001896284,0.001071325,0.00002126796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003037762,"about_ca_system_score_gemma":0.0002704033,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01202206,"about_ca_topic_score_gemma":0.01963238,"domain_scores_codex":[0.9968428,0.0003742923,0.0007992135,0.0009640371,0.0005272946,0.0004923719],"domain_scores_gemma":[0.998553,0.0001655751,0.0001578237,0.001010251,0.00003585207,0.00007750971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001083354,0.0002877507,0.9039403,0.0002039003,0.00001929973,0.00004179635,0.03536022,0.002125182,0.00003356115,0.01015477,0.000231726,0.04759067],"study_design_scores_gemma":[0.0002037153,0.00001173091,0.9164143,0.0002388427,0.000002269602,0.000001267135,0.001784951,0.03648765,0.00006173506,0.04445036,0.00002189794,0.0003212671],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970815,0.00007720598,0.02025863,0.007204119,0.0006856438,0.0005405645,0.000008601722,0.0002099596,0.0002002811],"genre_scores_gemma":[0.9952176,0.00007378212,0.004162901,0.00008907565,0.00003144928,0.000120282,0.00005188667,0.00001099196,0.0002420269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0472694,"threshold_uncertainty_score":0.9999547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.113672685612293,"score_gpt":0.2926149016020094,"score_spread":0.1789422159897164,"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."}}