{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003835284,0.00005159462,0.0001004918,0.0001068276,0.0002403186,0.0001027935,0.00005094663,0.0000365198,0.0000139888],"category_scores_gemma":[0.0007341874,0.00005196132,0.0000507866,0.0001681123,0.00008321615,0.0002497223,0.000009586339,0.00009590522,9.44445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004218375,"about_ca_system_score_gemma":0.0002141032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001877106,"about_ca_topic_score_gemma":0.000009931614,"domain_scores_codex":[0.9994212,0.00001945812,0.0002162737,0.0000612036,0.0001783524,0.0001034661],"domain_scores_gemma":[0.9985867,0.0005943297,0.00007904461,0.00001246932,0.0006779452,0.00004955169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001774727,0.00009619481,0.001905252,0.00006804097,0.0002768033,0.00001319377,0.004081733,0.3798856,0.0001129868,0.5658892,0.000748639,0.04674484],"study_design_scores_gemma":[0.001007163,0.000397668,0.0213384,0.0009024343,0.0004492998,0.00004788621,0.01445786,0.7650312,0.0006929749,0.06162088,0.1336936,0.0003607052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1059827,0.0002780684,0.8883809,0.001156624,0.000544158,0.0001234834,0.00001398179,0.00001583768,0.003504253],"genre_scores_gemma":[0.9948828,0.00001436768,0.004378398,0.00007449904,0.0003084792,0.000002742354,0.000002118292,0.000003660896,0.0003329007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8889001,"threshold_uncertainty_score":0.2118921,"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."}}