{"id":"W3165804018","doi":"","title":"The Unpredictable Nature of Termination Notice: A Data Science Experiment","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Notice; Predictability; Set (abstract data type); Computer science; Data set; Law; Artificial intelligence; Data science; Political science","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.005507372,0.00006923926,0.00008825833,0.00003511761,0.001676729,0.0001670719,0.002035687,0.00007182219,0.00003141552],"category_scores_gemma":[0.001725976,0.00005016467,0.00003247318,0.0006121887,0.001063235,0.0008725235,0.0001856852,0.001242797,0.00001436365],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005422233,"about_ca_system_score_gemma":0.005781518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002780055,"about_ca_topic_score_gemma":0.001781895,"domain_scores_codex":[0.9971787,0.0001224064,0.0002569941,0.000209563,0.0008974811,0.00133486],"domain_scores_gemma":[0.9990249,0.0001458789,0.0001902342,0.0002681887,0.0002494235,0.0001213469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003103498,0.00002981318,0.0002474444,0.000001666368,0.0000187444,0.00000104921,0.008685711,0.00003376364,0.004449786,0.9567108,0.0003112077,0.02947901],"study_design_scores_gemma":[0.0003598523,0.0008317867,0.0002158207,0.00006896072,0.00009160669,0.00004054759,0.3591661,0.01079304,0.04644881,0.2924902,0.2889546,0.0005386283],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7008676,0.02641013,0.02644061,0.1535551,0.0049838,0.001684022,0.00003737991,0.0002523018,0.08576909],"genre_scores_gemma":[0.9969286,0.002075978,0.00009136163,0.0001607483,0.0005065201,0.000001326681,0.000001047058,0.000006094877,0.0002282646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6642205,"threshold_uncertainty_score":0.9998548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04154863650490669,"score_gpt":0.3845700407867131,"score_spread":0.3430214042818064,"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."}}