{"id":"W2981119880","doi":"10.1017/mor.2019.34","title":"An Anatomy of Bengaluru's ICT Cluster: A Community Detection Approach","year":2019,"lang":"en","type":"article","venue":"Management and Organization Review","topic":"Innovation and Socioeconomic Development","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Information and Communications Technology; Cluster (spacecraft); Horizontal and vertical; Business; Economic geography; Community structure; Knowledge management; Industrial organization; Bridge (graph theory); Relation (database); Network analysis; Marketing; Economics; Computer science; Geography; Ecology; Data mining; Engineering","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":[0.0007016232,0.0001641227,0.0002930754,0.007511714,0.001339376,0.00239142,0.0009401543,0.0005044398,0.003647557],"category_scores_gemma":[0.004764027,0.0001771332,0.0002587753,0.007896395,0.001295743,0.001398873,0.001682951,0.0002440794,0.0004889057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003682963,"about_ca_system_score_gemma":0.001531525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07523074,"about_ca_topic_score_gemma":0.05303828,"domain_scores_codex":[0.9994593,0.0001352489,0.0000272852,0.0001257024,0.0001027386,0.0001497207],"domain_scores_gemma":[0.9973634,0.0009736394,0.0005464232,0.000217645,0.0006983735,0.000200552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006810874,0.0001028405,0.3816562,0.001134478,0.0002020991,0.00608715,0.05508728,0.02135371,0.02501799,0.1563659,0.01609372,0.3362175],"study_design_scores_gemma":[0.00004225475,0.0001380359,0.7201485,0.0003802478,0.0001821533,0.002815334,0.04078253,0.05807543,0.006460776,0.04089898,0.1299415,0.0001342129],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049631,0.002854631,0.02973363,0.002067229,0.00006813485,0.0002794299,0.002482769,0.000347981,0.05720303],"genre_scores_gemma":[0.9901854,0.000306154,0.005426173,0.00003405905,0.00001462437,0.00005575632,0.0004446933,0.00001952276,0.003513548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07523074,"threshold_uncertainty_score":0.1495857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008921619646886578,"score_gpt":0.2239058830170837,"score_spread":0.2149842633701971,"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."}}