{"id":"W4408687949","doi":"10.32714/ricl.13.02.04","title":"Same, same, but erm sort of different? Comparing three kinds of fluencemes across Australian, British, Canadian, and New Zealand English","year":2025,"lang":"en","type":"article","venue":"Research in Corpus Linguistics","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"sort; Genealogy; History; Computer science; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001644929,0.0001168027,0.000415794,0.0003226342,0.0003080489,0.0001250867,0.0004402035,0.0002326825,0.00006552901],"category_scores_gemma":[0.02446514,0.0001387239,0.00004200065,0.000674063,0.0009177183,0.00002111075,0.0001647407,0.0004839179,0.000001585294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002283906,"about_ca_system_score_gemma":0.001834105,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8171517,"about_ca_topic_score_gemma":0.9051162,"domain_scores_codex":[0.9976019,0.000221851,0.0005196661,0.0003118637,0.000557463,0.0007873112],"domain_scores_gemma":[0.9968137,0.0009150729,0.0001163972,0.0002433591,0.001529051,0.0003824568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003527658,0.00006758186,0.8905426,0.0001022359,0.00003127095,0.00005949728,0.005472695,0.00001709172,0.00005281228,0.09665722,0.004306498,0.002655199],"study_design_scores_gemma":[0.002154026,0.000156369,0.6442232,0.0008507697,0.00003761015,0.00000306008,0.005010256,0.0003818662,0.0004497493,0.08016877,0.2661859,0.0003784877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500299,0.0003171625,0.00004921029,0.000803318,0.002390203,0.0005404913,0.0002116327,0.00003420886,0.04562385],"genre_scores_gemma":[0.9896315,0.0003516834,0.0004017063,0.0000536636,0.0006349453,0.000007090233,0.00001862326,0.00001033176,0.008890416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2618794,"threshold_uncertainty_score":0.9837522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217247000286646,"score_gpt":0.4163803788728151,"score_spread":0.2946556788441504,"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."}}