{"id":"W4224217491","doi":"10.3390/languages7020091","title":"Less Direct, More Analytical: Eye-Movement Measures of L2 Idiom Reading","year":2022,"lang":"en","type":"article","venue":"Languages","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linguistics; Literal and figurative language; Computer science; Verb; Reading (process); Phrase; Comprehension; Meaning (existential); Eye tracking; Parsing; Artificial intelligence; Natural language processing; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004218209,0.0001344665,0.0002629281,0.0001476146,0.0001129852,0.00001432151,0.0002161505,0.00004648824,0.002992615],"category_scores_gemma":[0.00005524728,0.0001211586,0.0001236851,0.0002884175,0.0000561222,0.0000354913,0.00009238197,0.0001728292,0.00002706644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004424758,"about_ca_system_score_gemma":0.00001543578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008051398,"about_ca_topic_score_gemma":0.00004881055,"domain_scores_codex":[0.9985826,0.0002262153,0.0002481002,0.0002930635,0.0003853003,0.000264738],"domain_scores_gemma":[0.9993253,0.00007758034,0.0001134599,0.0003801891,0.00004524346,0.00005818283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001053588,0.003155409,0.08830208,0.0003533155,0.003011176,0.002266497,0.226552,0.0002167785,0.0774776,0.06604373,0.05946032,0.4721075],"study_design_scores_gemma":[0.00361899,0.0008071232,0.1556051,0.0001133058,0.0007291357,0.00005187577,0.7658337,0.0001793682,0.03711104,0.002244581,0.03239364,0.001312163],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8524626,0.003212157,0.0001395255,0.0002957899,0.0004085785,0.0001909275,0.0001117717,0.0001166798,0.1430619],"genre_scores_gemma":[0.9894226,0.00001421063,0.00007063949,0.0007548933,0.0001559016,0.0000895433,0.00006283524,0.00002721385,0.009402186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5392817,"threshold_uncertainty_score":0.9979188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03707933346251501,"score_gpt":0.3367099011247807,"score_spread":0.2996305676622657,"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."}}