{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0354374,0.0007431159,0.0006555417,0.004960454,0.002734991,0.01452455,0.004600558,0.006091139,0.01734376],"category_scores_gemma":[0.0800043,0.0007363605,0.001305047,0.00377267,0.01403857,0.02434878,0.006738103,0.007960838,0.00491948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01107453,"about_ca_system_score_gemma":0.02048464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00786634,"about_ca_topic_score_gemma":0.006171459,"domain_scores_codex":[0.9732477,0.0157876,0.00132888,0.002319067,0.006422877,0.0008938822],"domain_scores_gemma":[0.8927045,0.06924608,0.002466911,0.01265702,0.01783971,0.005085753],"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":[0.00003494773,0.000247489,0.001477897,0.00134622,0.00002152359,0.0001003559,0.006570766,0.002382916,0.001652818,0.7888621,0.02212172,0.1751813],"study_design_scores_gemma":[0.00003717239,0.0001319338,0.001277242,0.002282059,0.00002506379,0.0002273839,0.00493469,0.01568127,0.003129759,0.4920478,0.4801744,0.00005121583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02404462,0.03770771,0.3655745,0.2838942,0.002332898,0.0007627059,0.0004626572,0.003552272,0.2816685],"genre_scores_gemma":[0.247679,0.03329738,0.6741889,0.01643767,0.001305477,0.0007422059,0.001004349,0.0008758076,0.02446927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0354374,"threshold_uncertainty_score":0.1874132,"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."}}