{"id":"W7127553611","doi":"","title":"The rise of AI in procedural jurisprudence: global innovations, legal frameworks, and future implications","year":2025,"lang":"en","type":"article","venue":"MyPrints@UOM (Mysore University Library)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Accountability; Adversarial system; Process (computing); Transformative learning; Normative; Legal aspects of computing; Automation; Shadow (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002067833,0.0001062693,0.0001364509,0.0001277166,0.0008534639,0.0001246232,0.0007100152,0.0002594425,0.00008260837],"category_scores_gemma":[0.0001470645,0.0001022001,0.00004650519,0.002183647,0.0009894514,0.00108908,0.0002790058,0.000434989,0.000008931438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240595,"about_ca_system_score_gemma":0.0006696621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547892,"about_ca_topic_score_gemma":0.003098636,"domain_scores_codex":[0.998917,0.0001469514,0.0002318209,0.0002554755,0.000178662,0.0002700624],"domain_scores_gemma":[0.9992619,0.0001653074,0.00009716134,0.0002706396,0.0001363226,0.00006864943],"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":[0.00003216639,0.00004011436,0.14294,0.0000079178,0.00001152025,0.000002138126,0.0008788347,0.000007862606,0.000008371745,0.836919,0.01028552,0.008866625],"study_design_scores_gemma":[0.00009450258,0.00001116579,0.1707689,0.00004503737,0.00001334971,3.336019e-7,0.009921866,0.00005904937,0.0001436135,0.05969815,0.7591175,0.0001265656],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6319304,0.0003489263,0.001636069,0.245356,0.000515668,0.0007227121,0.00004462123,0.0001733014,0.1192724],"genre_scores_gemma":[0.9937034,0.0005549527,0.001046335,0.0006752957,0.0001165345,0.000002626161,0.00000454241,0.000005232951,0.003891124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7772208,"threshold_uncertainty_score":0.656424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007400363101917744,"score_gpt":0.2861770126687629,"score_spread":0.2787766495668452,"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."}}