{"id":"W4248367291","doi":"10.4018/978-1-5225-7507-8.ch037","title":"Tracking Children's Interactions With Traditional Text and Computer-Based Early Literacy Media","year":2018,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Child Development and Digital Technology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Tracking (education); Computer science; Software; Literacy; Early literacy; Focus (optics); Multimedia; Psychological intervention; Key (lock); Eye tracking; Human–computer interaction; Psychology; Pedagogy; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005612589,0.0004082083,0.0002555002,0.001267609,0.0004259388,0.001890313,0.0004892157,0.0007769572,0.005766864],"category_scores_gemma":[0.002279402,0.0002662887,0.0002575876,0.001303073,0.0006271023,0.001975247,0.00103206,0.0006925265,0.001891807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000587192,"about_ca_system_score_gemma":0.0004713399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003160808,"about_ca_topic_score_gemma":0.006778472,"domain_scores_codex":[0.9996802,0.00004466158,0.00002514659,0.00008217373,0.0001285621,0.00003931017],"domain_scores_gemma":[0.9990294,0.0007050462,0.0001196232,0.00004318807,0.00007234435,0.00003050493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000897114,0.0002070337,0.0464034,0.001398932,0.00002670125,0.001077189,0.06240632,0.0006687975,0.04949149,0.01626808,0.007676135,0.8142861],"study_design_scores_gemma":[0.00002721296,0.0006066891,0.4780832,0.003189939,0.0001121305,0.007018365,0.03508337,0.001611189,0.05036493,0.01356568,0.4101332,0.0002041585],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7126767,0.02083742,0.03312431,0.001343161,0.0001659024,0.0002265514,0.002892625,0.001094827,0.2276385],"genre_scores_gemma":[0.7831909,0.04560895,0.06778845,0.0005639506,0.00006499731,0.0005538245,0.002613826,0.0004800337,0.09913508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005766864,"threshold_uncertainty_score":0.01929212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02514646188386572,"score_gpt":0.2592361895490631,"score_spread":0.2340897276651974,"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."}}