{"id":"W4288942986","doi":"10.2139/ssrn.4176775","title":"Full Steam Ahead: Steamships, Global Migration and Regional Innovation","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Economic geography; Business; Regional science; Economics; Geography","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":[0.0009149354,0.0001449026,0.0002436184,0.0008476392,0.001602942,0.003600471,0.0003469035,0.001443274,0.02207105],"category_scores_gemma":[0.003802452,0.00006700542,0.0001875775,0.002099609,0.002531908,0.004102574,0.00195146,0.001368435,0.0005356421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074033,"about_ca_system_score_gemma":0.001680667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008437465,"about_ca_topic_score_gemma":0.019612,"domain_scores_codex":[0.9996861,0.0001244348,0.000007936328,0.00002678661,0.00003366138,0.0001210548],"domain_scores_gemma":[0.997824,0.001007209,0.0004143022,0.0000647382,0.0001124871,0.0005772387],"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":[0.0004701611,0.0003242812,0.1562802,0.0003509626,0.00008118903,0.0007466136,0.0349768,0.002287145,0.0003179309,0.6499169,0.04481511,0.1094326],"study_design_scores_gemma":[0.00008094133,0.0002021208,0.2261388,0.000645944,0.0001015931,0.0003614624,0.1977799,0.004026471,0.0002942827,0.4558786,0.1144135,0.00007639503],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8038816,0.009453507,0.001338713,0.08392596,0.0006678329,0.00001482989,0.0002658679,0.00002362557,0.100428],"genre_scores_gemma":[0.9916657,0.001689139,0.00007591386,0.0005587268,0.0002115111,0.000004853992,0.0000346853,0.000005007821,0.005754474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02207105,"threshold_uncertainty_score":0.07383507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425406562298319,"score_gpt":0.2827646245590456,"score_spread":0.2685105589360624,"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."}}