{"id":"W1538278780","doi":"10.3968/4892","title":"Linguistic Functional Feature Analysis of English Legal Memorandum","year":2014,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Discourse Analysis in Language Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Memorandum; Systemic functional linguistics; Systemic functional grammar; Linguistics; Theme (computing); Sentence; Mood; Perspective (graphical); Feature (linguistics); Psychology; Sociology; Computer science; Political science; Grammar; Social psychology; Artificial intelligence; Law; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003406759,0.0001015345,0.0001046765,0.002493566,0.0007740116,0.0008537946,0.0002288726,0.0001847881,0.002721145],"category_scores_gemma":[0.002891123,0.00006752357,0.0001393424,0.001563236,0.0008284813,0.0007404954,0.0004893881,0.0002334742,0.0002432382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007594075,"about_ca_system_score_gemma":0.0003245048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002380476,"about_ca_topic_score_gemma":0.002576893,"domain_scores_codex":[0.9997117,0.0001021515,0.00002469396,0.00005447932,0.00007239811,0.00003468839],"domain_scores_gemma":[0.9983369,0.0009136997,0.0002148973,0.0001221805,0.0003625746,0.00004963276],"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":[0.0006343732,0.0001910251,0.1813801,0.0008753866,0.00007692342,0.006057306,0.1205208,0.00161331,0.1136962,0.1202232,0.006582327,0.4481491],"study_design_scores_gemma":[0.00002161225,0.000241706,0.7720494,0.0002203842,0.000107143,0.004671988,0.05304817,0.01357227,0.03804436,0.02084517,0.09708442,0.00009348337],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783776,0.0002337559,0.008281453,0.0001661618,0.00001330855,0.00002837547,0.000456346,0.00008846408,0.0123544],"genre_scores_gemma":[0.9948684,0.00004217702,0.002909846,0.000009260725,0.000006997846,0.0000202338,0.0003628355,0.00002193211,0.001758281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002721145,"threshold_uncertainty_score":0.00910306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817127735979387,"score_gpt":0.2914345968462626,"score_spread":0.2732633194864688,"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."}}