{"id":"W2938438990","doi":"10.1111/caje.12386","title":"Increasing earnings inequality and the gender pay gap in Canada: Prospects for convergence","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Earnings; Convergence (economics); Gender pay gap; Inequality; Economics; Representation (politics); Gender inequality; Labour economics; Demographic economics; Gender equality; Gender gap; Political science; Sociology; Economic growth; Wage; Accounting","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.003035651,0.0001754502,0.0005804017,0.003820035,0.00400396,0.004086528,0.00109546,0.000647014,0.005156017],"category_scores_gemma":[0.01118343,0.0001229093,0.0003767289,0.0053665,0.001990582,0.001435627,0.002976689,0.001191069,0.0001530607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02880789,"about_ca_system_score_gemma":0.04653453,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9760973,"about_ca_topic_score_gemma":0.9771211,"domain_scores_codex":[0.9975327,0.0001727319,0.00005881307,0.0002193962,0.0007903608,0.001225953],"domain_scores_gemma":[0.993073,0.0009269898,0.000814407,0.0002305839,0.003698732,0.001256217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003522722,0.00008073505,0.7684554,0.0001569087,0.00009289898,0.0005395128,0.01230825,0.001884724,0.0004689383,0.06994462,0.01018415,0.1355316],"study_design_scores_gemma":[0.0000096296,0.00002658762,0.9689855,0.00022544,0.00002368641,0.00007149655,0.01149224,0.002044968,0.0002068731,0.005651319,0.0112356,0.00002674366],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9204682,0.005497799,0.0009814609,0.02546841,0.0001088657,0.00003598628,0.002021535,0.00004117781,0.04537666],"genre_scores_gemma":[0.996991,0.0007093161,0.0001988185,0.0003418325,0.00002186396,0.000004856098,0.0002187461,0.000004741088,0.001508804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02880789,"threshold_uncertainty_score":0.2090169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010822441046318,"score_gpt":0.1819450160130049,"score_spread":0.08086277190837309,"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."}}