{"id":"W3011768225","doi":"10.21083/ruralreview.v2i1.6072","title":"Surveying Municipal Data Systems in Ontario","year":2018,"lang":"en","type":"article","venue":"Rural Review Ontario Rural Planning Development and Policy","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Globe; The Internet; Order (exchange); Rural area; Survey data collection; Phenomenon; Political science; Developing country; Public relations; Business; Regional science; Environmental planning; Geography; Economic growth; Computer science; World Wide Web; Economics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.002072762,0.0001588001,0.0002630446,0.00251115,0.003460959,0.002069828,0.000786798,0.0003600617,0.003384172],"category_scores_gemma":[0.008623721,0.0002849219,0.0002201594,0.01477799,0.0008835117,0.0008313349,0.001261789,0.0003097878,0.0003117913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05560745,"about_ca_system_score_gemma":0.06622886,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9901362,"about_ca_topic_score_gemma":0.9958785,"domain_scores_codex":[0.9972555,0.0004277506,0.0002246492,0.0002346729,0.00140135,0.0004560295],"domain_scores_gemma":[0.9895023,0.00144354,0.001816282,0.0002859284,0.006174488,0.0007774661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001262357,0.00009783026,0.7741078,0.002297468,0.0001225423,0.000710909,0.08469624,0.0008337356,0.001442826,0.007669487,0.03187278,0.09602203],"study_design_scores_gemma":[0.0000123417,0.00005862024,0.8143561,0.0003947376,0.00004751722,0.00008614102,0.05639037,0.0004956017,0.000336632,0.0002357251,0.1275592,0.00002692],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9369128,0.005836131,0.0007388241,0.003739801,0.00003290906,0.0004965586,0.01264408,0.00003930676,0.0395595],"genre_scores_gemma":[0.9681567,0.007996621,0.001790336,0.0005510564,0.00002078587,0.0003142351,0.00464901,0.00001976823,0.01650153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05560745,"threshold_uncertainty_score":0.4034622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183740823660809,"score_gpt":0.369995347025502,"score_spread":0.251621264659421,"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."}}