{"id":"W2143940456","doi":"10.1145/2488388.2488439","title":"Organizational overlap on social networks and its applications","year":2013,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Social network (sociolinguistics); Organizational network analysis; Connection (principal bundle); Data science; Face (sociological concept); Enhanced Data Rates for GSM Evolution; Social network analysis; World Wide Web; Knowledge management; Social media; Artificial intelligence; Organizational learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002914357,0.001096918,0.001417658,0.005693709,0.001945354,0.002898172,0.001564784,0.001966003,0.003164961],"category_scores_gemma":[0.02043263,0.0008249156,0.0009959342,0.007118688,0.00262922,0.006924426,0.004913692,0.001798105,0.0006296301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002069748,"about_ca_system_score_gemma":0.0007385116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007666716,"about_ca_topic_score_gemma":0.004190285,"domain_scores_codex":[0.9966983,0.00123758,0.0001669741,0.0008043153,0.0008928046,0.0002000541],"domain_scores_gemma":[0.9803407,0.01369358,0.002304077,0.001693957,0.001062875,0.000904694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002945709,0.0003062466,0.05324088,0.0004910328,0.000292878,0.0005585265,0.00242144,0.323326,0.001794694,0.4431081,0.007666368,0.1664992],"study_design_scores_gemma":[0.00001463714,0.00003447125,0.004947704,0.00005150578,0.00003078591,0.0002263265,0.0004966164,0.6748211,0.0004699355,0.3137704,0.005108087,0.00002833606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2281187,0.007026759,0.7372743,0.004719716,0.0002109191,0.0002112885,0.001460712,0.001548237,0.01942936],"genre_scores_gemma":[0.9262377,0.001948291,0.06770313,0.000191544,0.0004827633,0.0001734289,0.0006931861,0.0001082239,0.002461731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007666716,"threshold_uncertainty_score":0.01541275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00960841445975208,"score_gpt":0.2428414374828133,"score_spread":0.2332330230230612,"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."}}