{"id":"W3107209022","doi":"10.3233/faia200865","title":"Plain Language Assessment of Statutes","year":2020,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Legal Language and Interpretation","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Readability; Statute; Operationalization; Plain language; Computer science; Legislature; Legislation; Proxy (statistics); Plain English; Rewriting; Artificial intelligence; Political science; Programming language; Law; Machine learning","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":[0.008653994,0.0003921195,0.0003668614,0.00691071,0.001044403,0.006912929,0.00119704,0.0007784854,0.009668224],"category_scores_gemma":[0.08020973,0.0002420281,0.0003846858,0.004160778,0.002295344,0.005808196,0.00163451,0.001299854,0.001848242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002371321,"about_ca_system_score_gemma":0.001894155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003588704,"about_ca_topic_score_gemma":0.004548017,"domain_scores_codex":[0.9893863,0.003473547,0.0008797194,0.0007790003,0.005296727,0.0001846324],"domain_scores_gemma":[0.9400312,0.03104394,0.005692275,0.004989716,0.0177678,0.0004750641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001424897,0.000137776,0.02384263,0.0006678535,0.00005778691,0.0002717301,0.01264188,0.007733458,0.006458226,0.3223751,0.02710141,0.5985696],"study_design_scores_gemma":[0.00003453806,0.000410739,0.08269626,0.002237804,0.0001136534,0.001145756,0.01292682,0.05341844,0.02686547,0.3998427,0.4200644,0.0002433838],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.216814,0.004165867,0.2770579,0.00434454,0.0006454341,0.000700569,0.004153009,0.002088824,0.4900299],"genre_scores_gemma":[0.8114754,0.001580266,0.1415874,0.0005438267,0.0002701535,0.0002738194,0.004083272,0.0006067253,0.03957906],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.009668224,"threshold_uncertainty_score":0.04576725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02910768323006172,"score_gpt":0.3378805018336785,"score_spread":0.3087728186036167,"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."}}