{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004361343,0.0006312792,0.0005309901,0.004201966,0.0007157254,0.003706793,0.0008360314,0.0007188842,0.003390338],"category_scores_gemma":[0.01902592,0.0002509503,0.0007031444,0.0073069,0.0008701796,0.004165182,0.00235694,0.001330791,0.0006096811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006529668,"about_ca_system_score_gemma":0.0009463816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01302549,"about_ca_topic_score_gemma":0.01505203,"domain_scores_codex":[0.9980908,0.0008668284,0.0001405218,0.000350058,0.0002977353,0.0002540889],"domain_scores_gemma":[0.9765542,0.01408045,0.004140538,0.002618831,0.001335737,0.001270352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003453125,0.000125708,0.9611833,0.0001633879,0.0004711048,0.0001716045,0.0009645908,0.007357737,0.0002477507,0.004053236,0.004238335,0.02098874],"study_design_scores_gemma":[0.00001140976,0.00009302568,0.93694,0.0002811847,0.0001908732,0.00008489688,0.00525721,0.02838587,0.0005893258,0.01938323,0.008723038,0.00005997775],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.936324,0.001305977,0.02415187,0.006917896,0.0001563166,0.0001204667,0.01716269,0.0001728155,0.01368798],"genre_scores_gemma":[0.990306,0.0002931528,0.004021258,0.0003059003,0.00008606643,0.00005488608,0.004392526,0.0000222462,0.0005178993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01302549,"threshold_uncertainty_score":0.02589935,"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."}}