{"id":"W4366758769","doi":"10.1163/17932548-12341477","title":"Brain Drain, Brain Gain and Brain Circulation: Emerging Trends and Patterns of Chinese Transnational Talent Mobility","year":2023,"lang":"en","type":"article","venue":"Journal of Chinese Overseas","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Mobilities; Diaspora; China; Circulation (fluid dynamics); Economic geography; Power (physics); Asian studies; Macro; Political science; Sociology; Economy; Economics; Gender studies; Social science; Engineering; Law","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":[0.0002716623,0.0001110564,0.0001351967,0.001714806,0.000511401,0.0008495204,0.0002258552,0.0001505518,0.002671292],"category_scores_gemma":[0.00109139,0.00005350388,0.0001198141,0.002228694,0.0006400869,0.001051258,0.0007817653,0.0002624855,0.0001136658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006152742,"about_ca_system_score_gemma":0.0006518625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01804214,"about_ca_topic_score_gemma":0.02204992,"domain_scores_codex":[0.9998844,0.00002001543,0.00001026043,0.00002540259,0.00002243153,0.0000375747],"domain_scores_gemma":[0.9994013,0.0001430292,0.0001879189,0.00003197735,0.0001299147,0.0001058639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001092701,0.0000328749,0.9037784,0.0001390269,0.00006542357,0.0005385385,0.02796319,0.0004968935,0.002580784,0.008391929,0.001064133,0.05483954],"study_design_scores_gemma":[0.000002103409,0.00003356854,0.9781773,0.00003399613,0.00001763933,0.0001431389,0.01636857,0.001228824,0.0003275797,0.001281103,0.002374438,0.00001163212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977514,0.0001624328,0.0001258003,0.0002168525,0.000003557983,0.000003225634,0.00009868976,0.000003151564,0.001634955],"genre_scores_gemma":[0.9993792,0.000128869,0.00004781519,0.000009961327,0.000004752734,0.000003733863,0.00005510995,8.101248e-7,0.0003697514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01804214,"threshold_uncertainty_score":0.03587425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163673152470063,"score_gpt":0.3154109418687883,"score_spread":0.299043626621782,"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."}}