{"id":"W3202200687","doi":"10.1177/00346446221093053","title":"The forest behind the tree: Heterogeneity in how U.S. Governor’s party affects black workers","year":2022,"lang":"en","type":"article","venue":"The Review of Black Political Economy","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Earnings; Wage; Economics; Governor; Ceteris paribus; Demographic economics; Distribution (mathematics); Regression discontinuity design; Labour economics; Allegiance; Political science; Politics; Law; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001914505,0.00007752457,0.0001895861,0.0006680628,0.000749103,0.0007947239,0.0002935273,0.0003505965,0.002705649],"category_scores_gemma":[0.005563368,0.00006988423,0.0003069039,0.0014605,0.0008199739,0.0007041791,0.0006600948,0.0005571287,0.0001833337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004979351,"about_ca_system_score_gemma":0.000594257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03073034,"about_ca_topic_score_gemma":0.05575288,"domain_scores_codex":[0.9990819,0.0005044633,0.00002587379,0.0001524942,0.0001249227,0.0001103711],"domain_scores_gemma":[0.9969614,0.001732419,0.0007158672,0.0001832133,0.0002417955,0.0001652855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002235888,0.0001001074,0.8486158,0.000179751,0.0004439823,0.0002044728,0.007525044,0.0006644254,0.0004562398,0.01803536,0.005180636,0.1183706],"study_design_scores_gemma":[0.000006230357,0.00004612853,0.9814007,0.0001778165,0.0001234653,0.00003851909,0.004422009,0.0004146342,0.000149029,0.005402828,0.007809788,0.000008861679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969614,0.01013543,0.001006056,0.007319263,0.00007496855,0.000009708948,0.0003742871,0.00000752928,0.01145875],"genre_scores_gemma":[0.9966827,0.001990973,0.0001109384,0.0004833762,0.00006929014,0.000003771127,0.00009004276,0.0000046793,0.0005641378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03073034,"threshold_uncertainty_score":0.06110293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462293803446095,"score_gpt":0.2491183347217021,"score_spread":0.2244953966872411,"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."}}