{"id":"W2802612561","doi":"10.1111/imig.12456","title":"Policies for Recruiting Talented Professionals from the Diaspora: India and China Compared","year":2018,"lang":"en","type":"article","venue":"International Migration","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Public Security of the People's Republic of China; Department of Science and Technology, Ministry of Science and Technology, India; Canadian Institute for Advanced Research; Department of Biotechnology, Ministry of Science and Technology, India; National Science Foundation","keywords":"Diaspora; China; Privilege (computing); Political science; Economic growth; Development economics; Power (physics); Race (biology); Economics; Sociology; Gender studies","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002899101,0.0001833092,0.000192317,0.001462782,0.003589934,0.003093437,0.0009948096,0.0009165193,0.005922024],"category_scores_gemma":[0.004486989,0.0001300754,0.0002495164,0.001304431,0.001781845,0.0006968961,0.003935299,0.0009631729,0.0003274975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0068481,"about_ca_system_score_gemma":0.0224963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07302792,"about_ca_topic_score_gemma":0.1079528,"domain_scores_codex":[0.9974215,0.0007522748,0.00007302428,0.0001055239,0.0002201912,0.001427541],"domain_scores_gemma":[0.9961348,0.0006573373,0.0004956776,0.0001573339,0.0003374759,0.002217508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006870305,0.000998602,0.6819738,0.0006659617,0.0001167253,0.00253326,0.06643487,0.003265851,0.00385238,0.1163719,0.01372454,0.109375],"study_design_scores_gemma":[0.000128257,0.0004381148,0.786415,0.000714043,0.00008519487,0.000397627,0.1570798,0.002464464,0.001988993,0.00545672,0.04474749,0.00008432391],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771581,0.0003404601,0.0001785937,0.005292874,0.0000343507,0.00008633682,0.00005975149,0.00001526353,0.01683421],"genre_scores_gemma":[0.9942811,0.0002837566,0.0001060043,0.0008624176,0.00001362902,0.00006451055,0.00003182407,0.000002683608,0.004354123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07302792,"threshold_uncertainty_score":0.1452057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03260868155990622,"score_gpt":0.363507337317143,"score_spread":0.3308986557572368,"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."}}