{"id":"W2766432544","doi":"10.5465/ambpp.2016.16251abstract","title":"Effects of Occupational Licensing and Unions on Compensation in Canada","year":2016,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Occupational and Professional Licensing Regulation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Unobservable; Occupational licensing; Compensation (psychology); Economics; Longitudinal data; Fixed effects model; Demographic economics; Panel data; Labour economics; Econometrics; Demography; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001839669,0.0002522649,0.0007006163,0.002478607,0.00351815,0.003046806,0.001457774,0.0009593167,0.0075331],"category_scores_gemma":[0.01172869,0.0002745473,0.001033682,0.003432381,0.001275651,0.0007657281,0.002092471,0.001423819,0.0005360464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08068278,"about_ca_system_score_gemma":0.1033466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9975035,"about_ca_topic_score_gemma":0.9981566,"domain_scores_codex":[0.9960606,0.0002856262,0.000140694,0.0003265906,0.00134858,0.00183797],"domain_scores_gemma":[0.9895462,0.001114454,0.001874687,0.0002630031,0.004143931,0.003057754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002666789,0.0001192515,0.9636346,0.00004754889,0.0001170815,0.0002099939,0.001504298,0.001630716,0.0001487273,0.004925343,0.006672781,0.020723],"study_design_scores_gemma":[0.00002522192,0.00003475646,0.9876072,0.00008473819,0.00006699405,0.00004763265,0.002841559,0.001767219,0.0001227361,0.0004405451,0.006928283,0.00003309533],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682714,0.002913912,0.0002186282,0.004690768,0.0000682989,0.00003615682,0.003963984,0.00004479758,0.01979205],"genre_scores_gemma":[0.9885903,0.0008289742,0.00009383789,0.0001885575,0.00001405478,0.000008237587,0.001304996,0.00001116989,0.00895978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08068278,"threshold_uncertainty_score":0.5853972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637151896145061,"score_gpt":0.2364771897321368,"score_spread":0.2101056707706861,"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."}}