{"id":"W4321611868","doi":"10.1007/s11192-023-04657-x","title":"Research mobility to the United States: a bibliometric analysis","year":2023,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"Canada Research Chairs","keywords":"China; Presidency; Political science; Politics; Cohort; Demographic economics; Medicine; Economics; Law","routes":{"ca_aff":true,"ca_fund":true,"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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00177683,0.0002231775,0.0005650712,0.02146175,0.001162009,0.002747954,0.0005104817,0.0005201914,0.002352852],"category_scores_gemma":[0.01265321,0.0001027145,0.0007186962,0.04589042,0.0004721619,0.002340207,0.002091889,0.0004397322,0.0004337929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001803786,"about_ca_system_score_gemma":0.002221085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06479662,"about_ca_topic_score_gemma":0.07043247,"domain_scores_codex":[0.9983295,0.0004682823,0.0002114691,0.0002272712,0.0005636858,0.0001999051],"domain_scores_gemma":[0.9930096,0.002294886,0.001893493,0.0002137502,0.002158438,0.000429799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001272313,0.00009015176,0.9493944,0.0002131991,0.0004376966,0.0002120844,0.001602245,0.001563678,0.0002525422,0.003145475,0.004756487,0.03820482],"study_design_scores_gemma":[0.00001142636,0.00008186329,0.9740105,0.0001728093,0.0003747783,0.0002253893,0.0062513,0.003148472,0.0002689724,0.0007876744,0.0146504,0.00001627649],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751057,0.003871341,0.0005455512,0.0008761604,0.0000446682,0.00003293394,0.003958453,0.00002280084,0.01554243],"genre_scores_gemma":[0.9930291,0.002143248,0.0005811572,0.00008995298,0.00006425671,0.00002515561,0.003021502,0.000009079152,0.001036676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9785383,"threshold_uncertainty_score":0.128839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8030659521882623,"score_gpt":0.6690166332988601,"score_spread":0.1340493188894022,"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."}}