{"id":"W4387014740","doi":"10.1007/s11192-023-04837-9","title":"An analysis of international mobility and research productivity in computer science","year":2023,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Regional science; China; Scopus; Economic geography; Demographic economics; Political science; Geography; Economics; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001453288,0.0002043225,0.0003385962,0.007282211,0.000570699,0.001723786,0.0003791938,0.0003490128,0.002898214],"category_scores_gemma":[0.01359358,0.00008844728,0.0003946952,0.01459434,0.0005998983,0.001766193,0.001685247,0.0004249618,0.0004746687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000831327,"about_ca_system_score_gemma":0.0005278785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007002672,"about_ca_topic_score_gemma":0.005060222,"domain_scores_codex":[0.9990705,0.0003061073,0.00008725721,0.0001371574,0.0001923805,0.0002066072],"domain_scores_gemma":[0.9868599,0.004991467,0.005382098,0.000552906,0.001203956,0.001009697],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001568575,0.0000519972,0.9595965,0.00009221995,0.0002010252,0.0002737695,0.001641746,0.005394323,0.0003365313,0.005234903,0.001309832,0.02571043],"study_design_scores_gemma":[0.00001221012,0.0001269266,0.9651339,0.0001061145,0.00009320611,0.0003880399,0.007721658,0.01331811,0.0004587591,0.004890939,0.007721022,0.00002915298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914328,0.0007844717,0.001728061,0.0003427899,0.00001203396,0.00001216757,0.001172342,0.00002512854,0.004490184],"genre_scores_gemma":[0.9989041,0.0001624427,0.0002347667,0.000005195539,0.00001142875,0.00000664576,0.0004110816,0.000003472298,0.0002607928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9985467,"threshold_uncertainty_score":0.01392382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.707261089982269,"score_gpt":0.6701808773690084,"score_spread":0.03708021261326055,"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."}}