{"id":"W4409402469","doi":"10.47852/bonviewjcce52024104","title":"Legal Text Analytics for Reasonable Notice Period Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Cognitive Engineering","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Notice; Period (music); Analytics; Computer science; Data science; Political science; Law; Philosophy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008738168,0.0006731886,0.000330262,0.003357502,0.0004064379,0.0007828156,0.0008680646,0.0006637815,0.004662453],"category_scores_gemma":[0.009224445,0.0001311501,0.0003496901,0.002047818,0.0002492002,0.001336242,0.0007122855,0.001182038,0.001950237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187735,"about_ca_system_score_gemma":0.001286645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02855885,"about_ca_topic_score_gemma":0.04733253,"domain_scores_codex":[0.999363,0.0001298814,0.0000639041,0.0001835443,0.0001879769,0.00007167329],"domain_scores_gemma":[0.9961354,0.001669165,0.000730719,0.0004291106,0.0007392316,0.000296482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007696064,0.0009153708,0.1529494,0.0003885381,0.00009670945,0.0007016718,0.0006659369,0.1147152,0.006479562,0.006350547,0.103563,0.6124044],"study_design_scores_gemma":[0.00004837702,0.0001247614,0.03669639,0.00005429213,0.00002167552,0.0001247087,0.0003309458,0.9310645,0.005112288,0.00928292,0.01710304,0.00003613577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6745467,0.002867705,0.1938147,0.007436104,0.0005395457,0.0006426938,0.06879444,0.02709362,0.02426455],"genre_scores_gemma":[0.8962633,0.0004108069,0.05588403,0.0002294392,0.0002141635,0.0001933441,0.0419442,0.0001982406,0.004662496],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02855885,"threshold_uncertainty_score":0.05678523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745856974857194,"score_gpt":0.3090653920958216,"score_spread":0.2916068223472496,"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."}}