{"id":"W3150273082","doi":"10.52324/001c.8076","title":"Explaining Canadian Regional Wage Differentials","year":2014,"lang":"en","type":"article","venue":"Review of Regional Studies","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Treasury Board of Canada Secretariat; Government of Canada","funders":"Government of Canada","keywords":"Wage; Economies of agglomeration; Economics; Context (archaeology); Human capital; Economic geography; Current Population Survey; Labour economics; Population; Capital (architecture); Demographic economics; Geography; Economic growth; Sociology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001493183,0.0004138367,0.0004805862,0.002391275,0.001551074,0.001321035,0.0009881548,0.0004701772,0.005853772],"category_scores_gemma":[0.005177674,0.0001733333,0.0007171151,0.004898267,0.0008142029,0.0005043071,0.0008022381,0.0006183482,0.000408584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02019008,"about_ca_system_score_gemma":0.01442759,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9805493,"about_ca_topic_score_gemma":0.9798869,"domain_scores_codex":[0.9992762,0.00007829836,0.00001646302,0.0001365551,0.000227175,0.0002653826],"domain_scores_gemma":[0.9985128,0.0003564356,0.0002063935,0.000123282,0.000660846,0.0001401486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001985168,0.0000640735,0.4363754,0.0003739208,0.000266714,0.0007551716,0.004429764,0.07231057,0.0009100746,0.2818613,0.03941112,0.1630434],"study_design_scores_gemma":[0.00005581909,0.00003971885,0.753222,0.0002732443,0.0001970691,0.0002063123,0.004337962,0.0935637,0.0004800422,0.0637629,0.08374188,0.0001193676],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8740643,0.006430505,0.02047554,0.007115416,0.0001605525,0.0000974035,0.00975743,0.0002725571,0.0816263],"genre_scores_gemma":[0.9880752,0.001726967,0.002543182,0.0001782697,0.00002481365,0.00002157828,0.001554861,0.00002437105,0.005850735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02019008,"threshold_uncertainty_score":0.1464899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0977992671404605,"score_gpt":0.2719924299140641,"score_spread":0.1741931627736036,"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."}}