{"id":"W2262121003","doi":"10.2139/ssrn.2469553","title":"A 19th Century Data Goldmine for Legal Scholars","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Brock University","funders":"","keywords":"Late 19th century; Law; Political science; Art; Aesthetics","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":[0.008266507,0.00012734,0.0001697128,0.00007666842,0.001153046,0.0002932186,0.001425747,0.0001110839,0.00009949625],"category_scores_gemma":[0.001795343,0.0001193318,0.00009393021,0.0002196062,0.0002353692,0.001059361,0.0001020822,0.001274558,0.0001012672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006868196,"about_ca_system_score_gemma":0.002818251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001082105,"about_ca_topic_score_gemma":0.02712109,"domain_scores_codex":[0.9960961,0.0002378187,0.0003170629,0.0003005387,0.0004586372,0.002589839],"domain_scores_gemma":[0.9988449,0.000225969,0.0001680631,0.0004061625,0.000177198,0.0001776727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003672717,0.00004188869,0.0002104687,0.000001823305,0.00003543015,6.279888e-7,0.0004642682,0.00000893632,0.0001194475,0.8948311,0.001076284,0.103173],"study_design_scores_gemma":[0.0001334766,0.0001588939,0.0000133797,0.0000111016,0.00002753496,0.00002361206,0.004797662,0.0003576282,0.00007015896,0.2117362,0.7825174,0.0001528969],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3159533,0.01078776,0.5022979,0.04786912,0.009767936,0.002617399,0.0001153667,0.0006168991,0.1099743],"genre_scores_gemma":[0.9900889,0.002296221,0.0006822032,0.0003328437,0.002631817,0.000006623915,0.0000150251,0.00002626748,0.003920082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7814412,"threshold_uncertainty_score":0.9906314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04575864792703246,"score_gpt":0.3660347236587241,"score_spread":0.3202760757316917,"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."}}