{"id":"W2283373875","doi":"10.1177/0002764215580585","title":"Social Networks in East and Southeast Asia I","year":2015,"lang":"en","type":"article","venue":"American Behavioral Scientist","topic":"Social Capital and Networks","field":"Social Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Guanxi; Kinship; Context (archaeology); China; Social capital; Sociology; Social network (sociolinguistics); Socialism; Hierarchy; Interpersonal ties; Political science; Political economy; Social science; Politics; Geography; Communism; Law","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0007069564,0.0001198835,0.0002158622,0.00007507495,0.0005481188,0.0001909695,0.0002256455,0.00007501698,0.00004783217],"category_scores_gemma":[0.0000267909,0.0001233841,0.00005169784,0.001125484,0.0024493,0.0002097289,0.00009723912,0.0001847625,0.00002689692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001517639,"about_ca_system_score_gemma":0.0001290067,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01806819,"about_ca_topic_score_gemma":0.02257126,"domain_scores_codex":[0.9983045,0.0001986651,0.0001712179,0.0003065139,0.0004547109,0.0005644143],"domain_scores_gemma":[0.9993724,0.00001777822,0.0001012749,0.00008280633,0.00007260247,0.0003531582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00009214799,0.0005058345,0.5107186,0.000002398252,0.000008527277,0.0001102273,0.1291292,0.00002553647,0.00002521638,0.04326196,0.01824704,0.2978732],"study_design_scores_gemma":[0.001266746,0.0003478108,0.4470624,0.00002601549,0.00005643018,0.000008421235,0.4889336,0.0002916272,0.000002168564,0.002021953,0.05903939,0.0009434642],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977563,0.0001429742,0.00004558753,0.001267994,0.0005070533,0.0001795102,0.000006454306,0.00007429136,0.0202131],"genre_scores_gemma":[0.9985152,0.00001092842,0.00008643077,0.00007055971,0.0005329268,0.00001627495,0.000006982452,0.00001172405,0.0007489223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3598044,"threshold_uncertainty_score":0.9952643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06569861544453476,"score_gpt":0.3564273917948637,"score_spread":0.290728776350329,"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."}}