{"id":"W4230999539","doi":"10.1080/03069400.2014.914732","title":"Training the Dragon®: the use of voice recognition software in the legal writing classroom","year":2014,"lang":"en","type":"article","venue":"The Law Teacher","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Software; Computer science; Psychology; Speech recognition; Multimedia; Programming language; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008613589,0.0009425962,0.0005712392,0.001652218,0.006003373,0.005268891,0.002789741,0.003021676,0.04384413],"category_scores_gemma":[0.01974945,0.0006721843,0.0004205644,0.0006968028,0.002305368,0.00803519,0.007751157,0.004658268,0.01646073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00286173,"about_ca_system_score_gemma":0.008399682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003214355,"about_ca_topic_score_gemma":0.01597448,"domain_scores_codex":[0.9930016,0.003003701,0.0002714742,0.0009956133,0.001847747,0.0008800337],"domain_scores_gemma":[0.9841942,0.005615347,0.0007524698,0.0007734231,0.002063079,0.006601435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002131683,0.002001737,0.003359912,0.0003879275,0.000006835874,0.001691328,0.05031995,0.0001749471,0.004616335,0.005179293,0.4638371,0.4682115],"study_design_scores_gemma":[0.000185513,0.001150378,0.00642283,0.001335765,0.00002440135,0.002359237,0.05299975,0.001433361,0.0050279,0.009016296,0.9198635,0.0001811022],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3842554,0.01168819,0.07071076,0.1344798,0.01331657,0.002110225,0.0006965661,0.01494865,0.3677939],"genre_scores_gemma":[0.5681886,0.006837321,0.110479,0.04115995,0.001833219,0.002704787,0.001075653,0.001861612,0.2658599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04384413,"threshold_uncertainty_score":0.1466733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2436303316079541,"score_gpt":0.3600191924736909,"score_spread":0.1163888608657368,"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."}}