{"id":"W4413939232","doi":"10.24908/iqurcp19907","title":"Research and Development towards Advancing Legal AI","year":2025,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Political science; Engineering ethics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.01478498,0.0002719322,0.0003722534,0.001295196,0.004126123,0.002053617,0.001253324,0.0003287464,0.0001212222],"category_scores_gemma":[0.003882523,0.0002772708,0.00004498255,0.002869192,0.006247729,0.001566179,0.001039549,0.001992344,0.0002172432],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001201372,"about_ca_system_score_gemma":0.006659404,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009858955,"about_ca_topic_score_gemma":0.002942094,"domain_scores_codex":[0.9925219,0.0004663815,0.0006369482,0.001039742,0.003009771,0.002325254],"domain_scores_gemma":[0.9913715,0.0008722976,0.00008195743,0.0002561002,0.006833315,0.000584845],"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":[0.00007657271,0.0001024566,0.002414527,0.0001118689,0.00003687751,0.00001290654,0.02184576,9.436118e-7,0.0007762261,0.9042219,0.01359183,0.05680814],"study_design_scores_gemma":[0.0001604314,0.0001617737,0.0004324633,0.0005111676,0.00000485548,0.000002109835,0.08624008,0.0001757861,0.0130105,0.3886335,0.5103152,0.0003521518],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2666042,0.0004085247,0.01028812,0.3117614,0.001020709,0.002804633,0.000003927274,0.000599125,0.4065094],"genre_scores_gemma":[0.9815982,0.001005819,0.001720022,0.0002550685,0.0003436912,0.0002524801,0.000002736556,0.00002876308,0.0147932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7149941,"threshold_uncertainty_score":0.9999679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.209263520104758,"score_gpt":0.4959511334508653,"score_spread":0.2866876133461073,"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."}}