{"id":"W3015626305","doi":"10.22452/mjlis.vol25no1.7","title":"Macro-level collaboration network analysis and visualization with Essential Science Indicators: A case of social science","year":2020,"lang":"en","type":"article","venue":"Malaysian Journal of Library & Information Science","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"Betweenness centrality; Centrality; Social network analysis; Field (mathematics); Clustering coefficient; Cluster analysis; Data science; Ranking (information retrieval); Regional science; Network science; Computer science; Network analysis; Similarity (geometry); Macro; Social network (sociolinguistics); Internationalization; Complex network; Average path length; Geography; World Wide Web; Information retrieval; Shortest path problem; Statistics; Business; Social media; Graph; Mathematics","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001913467,0.0003145609,0.000230165,0.007393056,0.001206466,0.003048087,0.0004121615,0.0006989281,0.002587317],"category_scores_gemma":[0.005829077,0.000173062,0.0004731398,0.009258311,0.0009330488,0.003490126,0.002068269,0.0006204019,0.0002726418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091795,"about_ca_system_score_gemma":0.0009148067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006868268,"about_ca_topic_score_gemma":0.0084755,"domain_scores_codex":[0.9986776,0.0006403166,0.00008087921,0.0001802486,0.0002976804,0.0001233823],"domain_scores_gemma":[0.9962619,0.002283189,0.0005175426,0.0003387622,0.0003831154,0.000215387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003125771,0.0002094848,0.2188083,0.001222683,0.0002958112,0.005286825,0.0567506,0.04331231,0.01089506,0.4410143,0.02240384,0.1994882],"study_design_scores_gemma":[0.00004822393,0.0001529634,0.1787803,0.0006733806,0.0002191415,0.002707275,0.05563362,0.3453881,0.006881165,0.2501333,0.1592183,0.0001641178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7220277,0.001237052,0.2144509,0.004171783,0.0001264,0.0003013792,0.00550835,0.000891974,0.05128455],"genre_scores_gemma":[0.9065827,0.0004758972,0.08945798,0.00006630125,0.00003628137,0.0001526868,0.001072466,0.00006506222,0.002090527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9926069,"threshold_uncertainty_score":0.01365662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006711227098889307,"score_gpt":0.2646125725971509,"score_spread":0.2579013454982615,"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."}}