{"id":"W3181543362","doi":"10.1016/j.giq.2021.101600","title":"Is big data used by cities? Understanding the nature and antecedents of big data use by municipalities","year":2021,"lang":"en","type":"article","venue":"Government Information Quarterly","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal","funders":"HEC Montréal","keywords":"Big data; Exploit; Urbanization; Business; Service (business); Population; Field (mathematics); Survey data collection; Data science; Marketing; Computer science; Economic growth; Computer security; Economics; Sociology; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005421204,0.000194543,0.0003609023,0.003167132,0.003951059,0.009603551,0.0009267737,0.001656009,0.00526559],"category_scores_gemma":[0.04980424,0.0006921584,0.0004159739,0.01017559,0.007575969,0.008982249,0.005338131,0.002906663,0.0003303558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007290085,"about_ca_system_score_gemma":0.01081858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1053292,"about_ca_topic_score_gemma":0.1072722,"domain_scores_codex":[0.9930085,0.003411663,0.0003651087,0.0005085298,0.001179548,0.001526802],"domain_scores_gemma":[0.9017475,0.05796775,0.0206258,0.003653702,0.00649495,0.009510306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009475772,0.0002788228,0.8898814,0.0001274808,0.0001212236,0.0004067222,0.03297092,0.001179112,0.0002415529,0.0593317,0.004662876,0.01070334],"study_design_scores_gemma":[0.00002767472,0.00007893959,0.6328619,0.0005806588,0.0001231615,0.0001402964,0.292125,0.004089546,0.0006117237,0.04470513,0.02459201,0.0000640188],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401311,0.0008686315,0.0009726922,0.03161243,0.00004919922,0.0000459425,0.0004399442,0.0000177066,0.02586234],"genre_scores_gemma":[0.9990024,0.0002976261,0.00008585049,0.0001934959,0.00001857304,0.00001050055,0.00006771833,0.000005566075,0.0003181894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1053292,"threshold_uncertainty_score":0.2094322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07484469330455518,"score_gpt":0.2390798291934476,"score_spread":0.1642351358888924,"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."}}