{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaresearch","bibliometrics","insufficient_payload"],"category_scores_codex":[0.3487032,0.0003367265,0.0007206841,0.9887933,0.002253247,0.01332616,0.01367962,0.0002296742,0.001176729],"category_scores_gemma":[0.3806984,0.0001951281,0.0006172929,0.9995631,0.0008058267,0.0007578257,0.006027134,0.001152707,0.01971723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006770681,"about_ca_system_score_gemma":0.0004983443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636603,"about_ca_topic_score_gemma":0.0001864938,"domain_scores_codex":[0.9201319,0.002395172,0.001744724,0.002726498,0.06943381,0.003567951],"domain_scores_gemma":[0.885332,0.06601363,0.0004302859,0.006325702,0.0386479,0.003250483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003339008,0.0003906965,0.2956139,0.000008855273,0.0001888814,0.00003867986,0.001099943,0.04564825,0.00005268309,0.0008521748,0.50724,0.1488325],"study_design_scores_gemma":[0.0001765561,0.0001876687,0.5808696,0.000002130659,0.00002660365,0.000001241232,0.002007274,0.08368258,0.00007061494,0.002061386,0.3307071,0.0002072009],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701576,0.0004970342,0.01383096,0.008682446,0.00137183,0.001097867,0.000284932,0.0002515086,0.003825785],"genre_scores_gemma":[0.985088,0.0007876728,0.000467117,0.0007191326,0.0001571317,0.0001245551,0.00009741748,0.00002994579,0.01252905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2852557,"threshold_uncertainty_score":0.9997363,"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."}}