{"id":"W3140916832","doi":"10.1093/jeg/lbab009","title":"Immigrant diversity, integration and worker productivity: uncovering the mechanisms behind ‘diversity spillover’ effects","year":2021,"lang":"en","type":"article","venue":"Journal of Economic Geography","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Diversity (politics); Productivity; Economic geography; Spillover effect; Immigration; Metropolitan area; Externality; Demographic economics; Extant taxon; Cultural diversity; Economics; Sociology; Geography; Economic growth; Microeconomics; Biology","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":[0.001758071,0.0002900408,0.0004034362,0.001325784,0.0008072036,0.001639006,0.0004870739,0.0005959405,0.006255726],"category_scores_gemma":[0.007023109,0.0001711273,0.0006050183,0.001639452,0.001063072,0.001010856,0.002381399,0.0008238197,0.0003245237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008687257,"about_ca_system_score_gemma":0.001045047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01643578,"about_ca_topic_score_gemma":0.01815725,"domain_scores_codex":[0.9993466,0.0003007562,0.0000267669,0.0001032741,0.00006839953,0.0001542522],"domain_scores_gemma":[0.994239,0.002984133,0.001373032,0.0004205384,0.0003660428,0.0006172966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001502941,0.0001975592,0.9690309,0.00009389051,0.0001921851,0.0001906706,0.003399397,0.001989662,0.0006825487,0.005443084,0.0006289294,0.01800088],"study_design_scores_gemma":[0.00001556703,0.0001396702,0.9829522,0.0001235759,0.000108234,0.00003703922,0.008256417,0.002601738,0.0003614652,0.003905914,0.001480259,0.00001794563],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952627,0.000309032,0.0008773396,0.0004985041,0.00001266612,0.00001110961,0.000168243,0.000007127906,0.002853171],"genre_scores_gemma":[0.9993303,0.00009301687,0.0001865866,0.00003603738,0.000009111103,0.000006792541,0.00005485044,0.000001199774,0.0002822036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01643578,"threshold_uncertainty_score":0.03268021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009034424212044152,"score_gpt":0.2245741849282152,"score_spread":0.215539760716171,"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."}}