{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002630865,0.0002578632,0.0004654305,0.0001355208,0.000630038,0.0002302696,0.0008647685,0.0001146884,0.0003752874],"category_scores_gemma":[0.0001589752,0.0002224605,0.00003301948,0.0004030264,0.0001877551,0.0008355542,0.0003301539,0.0003285385,0.00008477104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254235,"about_ca_system_score_gemma":0.002375452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9781009,"about_ca_topic_score_gemma":0.9952546,"domain_scores_codex":[0.9975891,0.0002334563,0.0005976703,0.0003083281,0.0005798835,0.0006916385],"domain_scores_gemma":[0.9989686,0.0001435808,0.0002187694,0.0003801623,0.00006351824,0.0002253777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002286904,0.00002377936,0.8987536,0.000205162,0.00004925483,0.000009815513,0.08255242,6.511151e-7,0.000002939542,0.004727451,0.00567354,0.007978537],"study_design_scores_gemma":[0.0002007706,0.00001915483,0.2836601,0.002692082,0.00001498152,0.000003956953,0.003578452,0.000007497007,9.232467e-7,0.00004195514,0.7095059,0.000274232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9389868,0.01683125,0.000001469368,0.002108823,0.0005361218,0.0005310508,0.000006059641,0.00005645762,0.04094198],"genre_scores_gemma":[0.9656261,0.004068649,0.0004245278,0.002483668,0.0008624579,0.00004024701,0.0005166628,0.00002505106,0.02595264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7038323,"threshold_uncertainty_score":0.9071675,"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."}}