{"id":"W4366814651","doi":"10.3386/w31157","title":"Bound by Ancestors: Immigration, Credit Frictions, and Global Supply Chain Formation","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Immigration; Supply chain; Economics; Business; Geography; Archaeology","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":[0.001222698,0.0002231592,0.0003425371,0.0008642536,0.001018547,0.001796016,0.0003900152,0.0005178956,0.01104055],"category_scores_gemma":[0.01159028,0.0001733444,0.0003789725,0.002029507,0.001249841,0.001977406,0.002138969,0.0009576867,0.0003143199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007781372,"about_ca_system_score_gemma":0.0009027481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01959389,"about_ca_topic_score_gemma":0.0247994,"domain_scores_codex":[0.9995276,0.0001927727,0.00003111022,0.00009958936,0.00004334714,0.0001055981],"domain_scores_gemma":[0.9936061,0.00251713,0.002618431,0.0005081363,0.0002382119,0.0005120841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001081954,0.0001063222,0.9426597,0.00005279556,0.0001466304,0.0003954167,0.001792546,0.006262676,0.0001837969,0.0252462,0.001374822,0.02167087],"study_design_scores_gemma":[0.00009338913,0.0001610563,0.8460808,0.0004261894,0.0003709681,0.0004951913,0.01311992,0.0354474,0.0007335306,0.08314543,0.01986006,0.00006592897],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887285,0.0005254269,0.002762593,0.001039831,0.00001788526,0.00001414836,0.0003636657,0.00001834902,0.006529557],"genre_scores_gemma":[0.9979267,0.0002107421,0.0004241175,0.00004904423,0.00001239895,0.000006877436,0.0002379332,0.000003908389,0.001128361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01959389,"threshold_uncertainty_score":0.03895968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2913780346386064,"score_gpt":0.5107010922187781,"score_spread":0.2193230575801717,"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."}}