{"id":"W125521770","doi":"10.2139/ssrn.2560305","title":"The Use of 'Use': Legislative Intent, Plain Meaning, & Corpus Linguistics","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Legal Language and Interpretation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Corpus linguistics; Linguistics; Meaning (existential); Legislature; Plain language; Quantitative linguistics; Natural language processing; Applied linguistics; Computer science; Political science; Philosophy; Epistemology; Law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006938136,0.0007132894,0.000736748,0.006377847,0.007489164,0.0141119,0.002052557,0.00392907,0.005508773],"category_scores_gemma":[0.0205644,0.001017237,0.0003262693,0.01133789,0.02953874,0.02183092,0.004041085,0.006101283,0.0003986292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004183773,"about_ca_system_score_gemma":0.002504535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01107812,"about_ca_topic_score_gemma":0.01479689,"domain_scores_codex":[0.9914773,0.00721207,0.000269505,0.0003680382,0.0004909738,0.0001820827],"domain_scores_gemma":[0.9787694,0.01839956,0.001152239,0.0008578236,0.0005910578,0.0002298635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001354983,0.000008857142,0.0004573537,0.0001172643,0.0000058347,0.0000522989,0.03498005,0.0001015516,0.00006498494,0.9543101,0.004699646,0.005188387],"study_design_scores_gemma":[0.00002277496,0.00002368782,0.004768461,0.001124138,0.00004562655,0.000445697,0.05637343,0.002912648,0.0006067202,0.8161352,0.1174776,0.00006415828],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1622809,0.06708152,0.06778815,0.1223924,0.001242034,0.00008741588,0.0014054,0.0003239308,0.5773982],"genre_scores_gemma":[0.9877479,0.003929379,0.003485703,0.0007324078,0.0003395966,0.00007516796,0.0001250228,0.0001316629,0.003433134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0141119,"threshold_uncertainty_score":0.0366928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06685703779046978,"score_gpt":0.3243335371579432,"score_spread":0.2574764993674734,"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."}}