{"id":"W3109956373","doi":"10.2139/ssrn.3734656","title":"AI and Administrative Law","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Administrative law; Law; Political science; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003712797,0.0001113185,0.0001303067,0.00001903995,0.0002292995,0.0002263549,0.0004966789,0.00004118652,0.00002840633],"category_scores_gemma":[0.00005022335,0.00008150721,0.00005189981,0.0001453335,0.0001044729,0.0005619153,0.0001009062,0.001094576,0.0000529682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092566,"about_ca_system_score_gemma":0.001024551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001671757,"about_ca_topic_score_gemma":0.00009631202,"domain_scores_codex":[0.9983116,0.00007133385,0.0001658179,0.0002281219,0.0001794579,0.00104362],"domain_scores_gemma":[0.9995639,0.00003041282,0.0000542495,0.0001136659,0.00006540747,0.0001723327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000182119,0.00001574868,0.0000145179,0.000002350967,0.00003921522,0.000006470048,0.001103735,0.000001373168,0.0004186779,0.9807551,0.001225934,0.01639867],"study_design_scores_gemma":[0.001154884,0.004861259,0.00003162834,0.0000203508,0.00002381597,0.001773114,0.0008927113,0.02809402,0.005016557,0.8331799,0.1244054,0.0005463592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005857332,0.003623734,0.9017424,0.06453636,0.0002233282,0.0001410622,0.00000109206,0.0001259049,0.02374881],"genre_scores_gemma":[0.9894861,0.0008744851,0.0004758744,0.008459983,0.00021934,0.000001157377,2.589068e-7,0.00000748749,0.000475343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9836287,"threshold_uncertainty_score":0.475545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270744047049677,"score_gpt":0.2541231654614977,"score_spread":0.22704876075653,"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."}}