{"id":"W4408469039","doi":"10.52783/jisem.v10i18s.2884","title":"Global Trends in Foreign Direct Investment: Findings from Bibliometric Analysis for Policy Recommendations","year":2025,"lang":"en","type":"article","venue":"Journal of Information Systems Engineering & Management","topic":"International Business and FDI","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Foreign direct investment; Bibliometrics; Regional science; Economics; Business; Political science; Geography; Computer science; Library science; Macroeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01506687,0.001082972,0.00237708,0.1388073,0.001814133,0.01012988,0.001479024,0.001221973,0.01301496],"category_scores_gemma":[0.09869986,0.0004159473,0.00349181,0.2368436,0.001394315,0.008350398,0.004005639,0.001308325,0.002475861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005466239,"about_ca_system_score_gemma":0.01580851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01180856,"about_ca_topic_score_gemma":0.01142293,"domain_scores_codex":[0.9835158,0.003341486,0.004330892,0.001331738,0.006666538,0.0008135812],"domain_scores_gemma":[0.8830112,0.08169112,0.01249356,0.002811212,0.01862693,0.001365962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002225419,0.0001620855,0.1768817,0.1197529,0.002583718,0.001106088,0.01090304,0.001670487,0.0007982427,0.0251244,0.1121122,0.5486826],"study_design_scores_gemma":[0.00005522648,0.0001274551,0.3569656,0.1145184,0.004069681,0.001307533,0.04422759,0.002394635,0.0009946515,0.02195244,0.4531769,0.0002098722],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.13824,0.4417267,0.008547572,0.06043416,0.002500773,0.002401548,0.2192204,0.0009687856,0.1259602],"genre_scores_gemma":[0.5764022,0.3275577,0.01863603,0.003334629,0.001513241,0.002962259,0.06425339,0.0002767204,0.005063675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8611927,"threshold_uncertainty_score":0.07968223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01230572657297759,"score_gpt":0.253477430818403,"score_spread":0.2411717042454254,"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."}}