{"id":"W4366962326","doi":"10.2139/ssrn.4426938","title":"Bound by Ancestors: Immigration, Credit Frictions, and Global Supply Chain Formation","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Immigration; Supply chain; Chain (unit); Business; Economics; Monetary economics; Political science; Marketing; Law; Physics","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":[0.001544664,0.0001955663,0.0006003681,0.001039303,0.001588236,0.003643865,0.0007458801,0.001707298,0.02984035],"category_scores_gemma":[0.01454555,0.000283508,0.0003441029,0.002022433,0.002829646,0.004250672,0.002516482,0.001566327,0.0005426379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001570399,"about_ca_system_score_gemma":0.00132974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01538108,"about_ca_topic_score_gemma":0.02081956,"domain_scores_codex":[0.999632,0.0001339372,0.00002315891,0.0000749973,0.00002846957,0.0001074212],"domain_scores_gemma":[0.9925221,0.004077655,0.002008703,0.0003666069,0.000255777,0.0007691923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005402627,0.0003711615,0.3088863,0.0002323345,0.0001973225,0.00166224,0.00722234,0.04945711,0.0003623738,0.5729179,0.005364644,0.05278603],"study_design_scores_gemma":[0.0001843917,0.0001440552,0.08585656,0.0003822605,0.0002207094,0.0004484035,0.01695823,0.06181218,0.0003031598,0.8211483,0.01245975,0.00008191786],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9692875,0.0009665554,0.005430117,0.004108421,0.00004247364,0.00002108179,0.0001577362,0.00002175222,0.0199643],"genre_scores_gemma":[0.9969157,0.0003140156,0.0003074291,0.00005513824,0.00002151054,0.000004660226,0.00003556619,0.000003877586,0.00234209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02984035,"threshold_uncertainty_score":0.09982592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01096000109829528,"score_gpt":0.2714645223923546,"score_spread":0.2605045212940594,"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."}}