{"id":"W3212482791","doi":"10.3389/fpsyg.2021.660796","title":"Human Ratings of Writing Quality Capture Features of Syntactic Variety and Transformation in Chinese EFL Learners’ Argumentative Writing","year":2021,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Argumentative; Psychology; Variety (cybernetics); Linguistics; Quality (philosophy); Verb; Similarity (geometry); Context (archaeology); Natural language processing; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009117184,0.0001500305,0.0004625786,0.0002710519,0.00005135563,0.00001220066,0.0001012042,0.0002298196,0.001254687],"category_scores_gemma":[0.0001526926,0.0001576002,0.00006787965,0.0004162373,0.0001457376,0.0001410845,0.00001857645,0.0005709131,0.000001123827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002873579,"about_ca_system_score_gemma":0.00001609505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002937433,"about_ca_topic_score_gemma":0.00008245652,"domain_scores_codex":[0.9977391,0.0008304464,0.0007167387,0.0003513034,0.0001137884,0.0002486722],"domain_scores_gemma":[0.9992142,0.0001582725,0.0003187032,0.0002116358,0.00005872808,0.00003844987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004842709,0.000607181,0.5731986,0.0004303247,0.0002345416,0.0002037751,0.3078365,0.00003334993,0.03417574,0.008117221,0.001324271,0.07335417],"study_design_scores_gemma":[0.003349343,0.00009270761,0.8094913,0.0001046998,0.00001686117,0.00008528108,0.1844935,0.00002545386,0.0004135885,0.001698586,0.00005150964,0.0001771597],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719532,0.004697247,0.002868961,0.0004019261,0.0003907063,0.0001601749,0.0000121155,0.00001634512,0.0194994],"genre_scores_gemma":[0.994873,0.00002173825,0.003397885,0.001508793,0.00003726974,0.0000164286,0.00005173725,0.00001526515,0.00007786912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2362926,"threshold_uncertainty_score":0.9996583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01627968750088248,"score_gpt":0.3729763510338099,"score_spread":0.3566966635329274,"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."}}