{"id":"W3163783173","doi":"10.7202/1076909ar","title":"Predicting Employment Notice Period with Machine Learning: Promises and Limitations","year":2021,"lang":"en","type":"article","venue":"McGill Law Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Notice; Predictability; Computer science; Artificial intelligence; Period (music); Law; Machine learning; Political science; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.01585341,0.00164383,0.001340395,0.003521589,0.001228117,0.004415546,0.003220763,0.002868659,0.001320321],"category_scores_gemma":[0.04493031,0.0005666774,0.001199591,0.004390904,0.001595618,0.005795362,0.001962356,0.005218895,0.001113779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00193975,"about_ca_system_score_gemma":0.002393898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01903663,"about_ca_topic_score_gemma":0.01726408,"domain_scores_codex":[0.9932154,0.003527299,0.0004771308,0.001400291,0.001028085,0.0003517869],"domain_scores_gemma":[0.9410202,0.04595811,0.002792609,0.006353424,0.002907997,0.0009676891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003444998,0.0006518122,0.1460592,0.0004090506,0.0003213889,0.0002264298,0.0005273559,0.4648097,0.0006834641,0.01037303,0.01802188,0.3575721],"study_design_scores_gemma":[0.00001703896,0.00003284587,0.006333936,0.0001090047,0.00002478007,0.00004408166,0.0002259314,0.967894,0.0005164855,0.020903,0.003863358,0.00003566473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5031332,0.01797537,0.4266754,0.02708184,0.001004595,0.0003462808,0.00668172,0.002830063,0.01427164],"genre_scores_gemma":[0.8770831,0.001803882,0.1119614,0.0009969148,0.0008070052,0.0001333674,0.00569708,0.0001292988,0.001387996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01903663,"threshold_uncertainty_score":0.0838418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07561691443842182,"score_gpt":0.3173000339906206,"score_spread":0.2416831195521987,"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."}}