{"id":"W3094897300","doi":"10.2478/jdis-2020-0027","title":"Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019","year":2020,"lang":"en","type":"article","venue":"Journal of Data and Information Science","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social network analysis; Bibliometrics; China; Institution; Field (mathematics); Political science; Regional science; Library science; Social science; Public relations; Sociology; Computer science; Social capital","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":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002153827,0.00008109646,0.0001997986,0.002467009,0.0001464657,0.001541486,0.001524136,0.00003507514,0.000007127929],"category_scores_gemma":[0.001270511,0.00007106471,0.00001695486,0.05542158,0.0001030463,0.02685139,0.0007138677,0.00009697939,0.00001860981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005574051,"about_ca_system_score_gemma":0.0002925051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008557155,"about_ca_topic_score_gemma":0.00007757976,"domain_scores_codex":[0.9980369,0.00003674669,0.0007762678,0.0002093799,0.000734348,0.0002063714],"domain_scores_gemma":[0.9983485,0.00011576,0.0003795383,0.0003187647,0.0005530319,0.0002844116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001170785,0.00005222703,0.03277967,0.00001465138,0.00005848725,0.00001407352,0.0116097,0.1040044,0.0004421912,0.08683275,0.004346851,0.7597279],"study_design_scores_gemma":[0.00005019558,0.0001959593,0.02153977,0.00001506864,0.00002438912,0.000006368989,0.001798864,0.9652153,0.000576231,0.004745896,0.005687722,0.0001442432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1274571,0.0001403457,0.8671218,0.004821555,0.0001930995,0.00008712112,0.00005689075,0.000008413225,0.0001136283],"genre_scores_gemma":[0.9330258,0.0003240003,0.06490885,0.001638457,0.00008966189,5.127801e-7,0.00001137978,9.433346e-7,4.132132e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8612109,"threshold_uncertainty_score":0.999495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0638513092071118,"score_gpt":0.359537235449634,"score_spread":0.2956859262425222,"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."}}