{"id":"W4293147763","doi":"10.1075/ijcl.20024.nai","title":"Handle it in-house?","year":2022,"lang":"en","type":"article","venue":"International Journal of Corpus Linguistics","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Sophistication; Vocabulary; Natural language processing; Context (archaeology); Corpus linguistics; Selection (genetic algorithm); Artificial intelligence; Resource (disambiguation); Key (lock); Lexical density; Lexical item; Word lists by frequency; Linguistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004438212,0.00006445302,0.0001225098,0.0003148159,0.00004780233,0.00002887925,0.0003987616,0.00002610209,0.02962054],"category_scores_gemma":[0.0008835258,0.00006761333,0.00007860919,0.0000957239,0.00002040678,0.00001613389,0.00006664395,0.0003917982,0.00005712004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000116451,"about_ca_system_score_gemma":0.00006435649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005468959,"about_ca_topic_score_gemma":0.00001134208,"domain_scores_codex":[0.9988542,0.0001142019,0.0004207374,0.00008257014,0.0004121147,0.0001161846],"domain_scores_gemma":[0.998939,0.0001795634,0.0003415234,0.00008539286,0.0004073592,0.00004714114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003471114,0.003432007,0.1467897,0.00001993831,0.00131656,0.0528747,0.06414574,0.01848488,0.001134,0.4116718,0.2321435,0.06451606],"study_design_scores_gemma":[0.00141155,0.0001542869,0.005998939,0.00001735955,0.00001242848,0.000879333,0.003184869,0.0001135496,0.00003106768,0.0009252746,0.9871777,0.00009363122],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7686142,0.008047077,0.00298804,0.003138657,0.07423307,0.0001716962,0.0000636849,0.00009693341,0.1426466],"genre_scores_gemma":[0.9909272,0.000007695644,0.0003358488,0.005266175,0.001904157,0.000002744748,0.000004441408,0.00001970237,0.001532085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7550342,"threshold_uncertainty_score":0.9712665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167538962131468,"score_gpt":0.3440635685827646,"score_spread":0.3223881789614499,"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."}}