{"id":"W4388229467","doi":"10.1111/1748-8583.12535","title":"Developing new understanding of how global talent flow impact individual and firm performance by using big data","year":2023,"lang":"en","type":"article","venue":"Human Resource Management Journal","topic":"International Student and Expatriate Challenges","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Human capital; Business; Revenue; Population; Set (abstract data type); Economic geography; Labour economics; Industrial organization; Demographic economics; Marketing; Economics; Economic growth; Sociology; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001213409,0.000119431,0.0001416821,0.000165099,0.001054758,0.0003976026,0.0006569973,0.00004486131,0.00004566847],"category_scores_gemma":[0.00001916311,0.0001103101,0.00004709503,0.0002915717,0.0001011659,0.00025336,0.0005727896,0.0001194201,0.000003017483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005052859,"about_ca_system_score_gemma":0.00005784506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002212412,"about_ca_topic_score_gemma":0.0001899053,"domain_scores_codex":[0.9983358,0.00007775104,0.0002094839,0.0001938007,0.0008390514,0.0003440583],"domain_scores_gemma":[0.9995027,0.0000295702,0.0001977592,0.0001255357,0.00002804502,0.0001163859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.000282445,0.0002391606,0.2362236,0.0005821721,0.004371716,0.0003094361,0.1670891,0.002724031,0.0001250878,0.06175263,0.2710791,0.2552216],"study_design_scores_gemma":[0.005215508,0.000383237,0.1132782,0.001523322,0.0004986572,0.00005200443,0.6942211,0.004903718,0.0000248272,0.01916129,0.1593312,0.001406932],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903511,0.0004823191,0.00157602,0.001540203,0.0002465193,0.0001426346,0.00003050839,0.00003501114,0.005595695],"genre_scores_gemma":[0.9970309,0.001269741,0.0002858525,0.00003169412,0.0005590686,3.660394e-7,0.00004464782,0.000009308096,0.0007683876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5271321,"threshold_uncertainty_score":0.8112456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3295599727100768,"score_gpt":0.3985389525668064,"score_spread":0.06897897985672963,"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."}}