{"id":"W3205605438","doi":"10.1007/s11192-022-04351-4","title":"Return migration of German-affiliated researchers: analyzing departure and return by gender, cohort, and discipline using Scopus bibliometric data 1996–2020","year":2022,"lang":"en","type":"article","venue":"Scientometrics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Toronto; University of New Brunswick","funders":"Bundesministerium für Bildung und Forschung; Max-Planck-Institut für demografische Forschung; Deutscher Akademischer Austauschdienst","keywords":"Scopus; German; Emigration; Demographic economics; Tracking (education); Bibliometrics; Construct (python library); Political science; Sociology; Geography; Library science; Economics; MEDLINE; Computer science","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006939437,0.0003023135,0.0005705617,0.01686109,0.0008686007,0.002705726,0.0007830995,0.0005506277,0.002351287],"category_scores_gemma":[0.03740074,0.0001801868,0.0007927683,0.02992734,0.0005839624,0.00206091,0.002215768,0.0005915462,0.0008687525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419127,"about_ca_system_score_gemma":0.002374774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02652024,"about_ca_topic_score_gemma":0.02699068,"domain_scores_codex":[0.9958456,0.0007240023,0.001117856,0.000528628,0.001084694,0.0006992986],"domain_scores_gemma":[0.9500191,0.01151755,0.02838549,0.002108363,0.006060181,0.001909281],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007798949,0.00002197694,0.9859493,0.000258549,0.0002343444,0.0001454783,0.001070155,0.0002790579,0.0001113213,0.0008141259,0.002087254,0.008950354],"study_design_scores_gemma":[0.000007916318,0.00003498748,0.9885492,0.000258912,0.0001109992,0.0001407405,0.004138796,0.000580354,0.0002260007,0.0005260526,0.005410461,0.00001553822],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738408,0.003386296,0.0006165148,0.001019705,0.00004689314,0.00004635858,0.01796903,0.00002947709,0.00304493],"genre_scores_gemma":[0.9874709,0.001566347,0.0004950683,0.0001039259,0.00005195978,0.00007678103,0.009319302,0.00001450747,0.0009012421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9930606,"threshold_uncertainty_score":0.05273175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1826908997125493,"score_gpt":0.3564212691382598,"score_spread":0.1737303694257105,"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."}}