{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005331089,0.00008805952,0.0001154615,0.00002526444,0.007134569,0.0002615461,0.0001031564,0.00004917975,0.0002206332],"category_scores_gemma":[0.0008311692,0.00007491106,0.00003566064,0.0001685584,0.0003300548,0.0003491987,0.00004070592,0.0003701463,0.00001430165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000789785,"about_ca_system_score_gemma":0.0000504754,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001368828,"about_ca_topic_score_gemma":0.1091815,"domain_scores_codex":[0.9987001,0.0002615855,0.0002122668,0.0001630462,0.0003897501,0.0002731815],"domain_scores_gemma":[0.9990464,0.000265931,0.0001170942,0.00006943684,0.0003040922,0.0001970771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000123934,0.0003908421,0.06529995,0.00002585356,0.0002031224,0.000734477,0.04528294,0.002979906,0.0006836744,0.8273578,0.00006398938,0.05685348],"study_design_scores_gemma":[0.0001966876,0.000233407,0.0006283558,0.00009338041,0.00007173468,0.0003135798,0.02927901,0.0006110747,0.002130928,0.001108026,0.9651119,0.0002219525],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3399824,0.000681119,0.0007079375,0.01240826,0.0006295643,0.0003126818,0.00001532362,0.0001725944,0.6450901],"genre_scores_gemma":[0.9962143,0.0002060071,0.002312785,0.00015087,0.0002223865,0.000005132355,0.000001487459,0.00001384259,0.0008732016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9650479,"threshold_uncertainty_score":0.994158,"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."}}