{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003233124,0.0000628932,0.00006267773,0.00003879354,0.0005178252,0.0001297612,0.0001627557,0.00005121563,0.0001719434],"category_scores_gemma":[0.0004612799,0.00004730936,0.00002867337,0.00009795878,0.0001735583,0.0002088075,0.00002406638,0.00005395955,0.000009465274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004820693,"about_ca_system_score_gemma":0.0000633126,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004969226,"about_ca_topic_score_gemma":0.06225402,"domain_scores_codex":[0.9992689,0.00008634464,0.0001687714,0.0001296465,0.0002414797,0.0001048787],"domain_scores_gemma":[0.9992502,0.000260781,0.0001302232,0.00006315949,0.000259362,0.00003627119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001529134,0.0001487406,0.101359,0.000005939773,0.0001200981,5.172428e-7,0.5236641,0.00001679508,0.002068699,0.3054884,0.0557657,0.01120915],"study_design_scores_gemma":[0.0007850761,0.0000579946,0.617497,0.0001220262,0.00002598993,8.21486e-7,0.06016836,0.0294985,0.000578269,0.01554235,0.2754592,0.000264447],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731259,0.00004632413,0.00169871,0.02208454,0.0006562472,0.0003537631,0.0001634715,0.00004058604,0.001830434],"genre_scores_gemma":[0.9955772,0.00004976053,0.0005487837,0.001051288,0.001155445,0.00004822875,0.0002775918,0.000005641743,0.001286015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.516138,"threshold_uncertainty_score":0.9548574,"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."}}