{"id":"W2967647151","doi":"10.1080/09588221.2019.1647251","title":"Exploring the frontiers of eye tracking research in language studies: a novel co-citation scientometric review","year":2019,"lang":"en","type":"article","venue":"Computer Assisted Language Learning","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Education, Nanyang Technological University; National Institute of Education; Nanyang Technological University; Paragon Testing Enterprises; Ministry of Education - Singapore; Max Planck Instituut voor Psycholinguïstiek; International Business Machines Corporation","keywords":"Citation; Adjective; Scientometrics; Tracking (education); Multitude; Eye tracking; Eye movement; Scopus; Computer science; Citation analysis; Citation index; Data science; Psychology; Noun; Artificial intelligence; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00444838,0.0001841099,0.0004636598,0.001833886,0.0002023793,0.00012051,0.001307169,0.00005910191,0.000004134518],"category_scores_gemma":[0.0006404294,0.0001421064,0.0001066121,0.006851793,0.0001641177,0.000502652,0.000479497,0.0009918939,0.00003819283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001732828,"about_ca_system_score_gemma":0.00005792998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005743301,"about_ca_topic_score_gemma":0.00000846376,"domain_scores_codex":[0.9970407,0.0005903849,0.0004713713,0.000593573,0.0007266252,0.0005773113],"domain_scores_gemma":[0.9978341,0.0008761886,0.0002492143,0.0007048313,0.0002868496,0.00004885409],"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":[0.00001168399,0.000200878,0.03689568,0.001273904,0.0001319493,0.0001065975,0.04648342,0.001664487,0.01002554,0.001127899,0.0005285182,0.9015495],"study_design_scores_gemma":[0.003254557,0.0009373585,0.8026934,0.0134452,0.00006468799,0.00008706599,0.0528757,0.1163298,0.005228433,0.00007408959,0.003770262,0.001239506],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8554422,0.02312193,0.1189933,0.0007628273,0.0006211425,0.0004715276,9.278021e-7,0.0002473588,0.0003387945],"genre_scores_gemma":[0.9759919,0.0004587942,0.02317671,0.0001420855,0.00005818489,0.00004405129,0.000004998107,0.00001802755,0.0001052164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9003099,"threshold_uncertainty_score":0.579493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.223935623871368,"score_gpt":0.4243013599204514,"score_spread":0.2003657360490834,"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."}}