{"id":"W2763626063","doi":"","title":"Open Data and Big Data Programs in Local Government Policy Analysis","year":2014,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Open data; Big data; Government (linguistics); Local government; Computer science; Open government; Data science; Business; Political science; Public administration; World Wide Web; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["open_science"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001997029,0.0003499406,0.0005227513,0.0007592224,0.0001953574,0.001720448,0.129309,0.0002322789,0.000002424282],"category_scores_gemma":[0.002700137,0.0003800968,0.00004357882,0.003311642,0.0002969114,0.008286307,0.516757,0.0006267835,0.00002908101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003728406,"about_ca_system_score_gemma":0.0001713129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002026036,"about_ca_topic_score_gemma":0.01765333,"domain_scores_codex":[0.9956287,0.0004403644,0.0003626362,0.002146351,0.0007373182,0.0006846078],"domain_scores_gemma":[0.9728469,0.0001855845,0.0002565366,0.02640521,0.00005170209,0.000254025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002114019,0.0001314525,0.8810581,0.00002177247,0.0001558257,0.0001094699,0.00004852645,0.000006449312,0.00001416282,0.0003497084,0.00002567541,0.1180577],"study_design_scores_gemma":[0.001705449,0.0001734031,0.9644012,0.0001604132,0.0002309638,0.00002967702,0.0002212495,0.004684102,0.0001426446,0.005688576,0.02171689,0.0008454622],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6555457,0.00005204518,0.3345222,0.008273919,0.0001529257,0.0004822407,0.0003520166,0.0003786372,0.000240313],"genre_scores_gemma":[0.9880704,0.00009060259,0.01075416,0.0003291472,0.00006086144,0.000001794713,0.0003914715,0.00002363298,0.0002779173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3874479,"threshold_uncertainty_score":0.9998651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2551241424000368,"score_gpt":0.3563462926011085,"score_spread":0.1012221502010717,"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."}}