{"id":"W2739252428","doi":"10.1007/s11205-017-1706-1","title":"Exploring Community Cohesion in Rural Canada Post-Extreme Weather: Planning Ahead for Unknown Stresses","year":2017,"lang":"en","type":"article","venue":"Social Indicators Research","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Community cohesion; Sense of place; Social capital; Tornado; Cohesion (chemistry); Vulnerability (computing); Sense of community; Sociology; Public relations; Social psychology; Psychology; Geography; Political science; Computer security; Computer science; Social science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.001753512,0.0004912475,0.000693273,0.001714044,0.01162655,0.003719031,0.002193886,0.0007044171,0.003489381],"category_scores_gemma":[0.005778255,0.0002548023,0.000479273,0.005414236,0.002038212,0.001885469,0.003609936,0.002095611,0.0001848704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0518623,"about_ca_system_score_gemma":0.14577,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936371,"about_ca_topic_score_gemma":0.9980881,"domain_scores_codex":[0.9983156,0.0002096153,0.00002508763,0.00009233419,0.0001923204,0.00116512],"domain_scores_gemma":[0.99405,0.0004531153,0.0004160471,0.0001168435,0.002306932,0.002657034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003006358,0.0005164111,0.832723,0.0004241315,0.0002094767,0.0004580473,0.07363185,0.001200871,0.0007171324,0.003954307,0.01811928,0.06774488],"study_design_scores_gemma":[0.00001313174,0.0001069203,0.7277801,0.0001800842,0.00003665271,0.00002247087,0.2632509,0.0004712928,0.000121785,0.0006937016,0.007284744,0.00003827582],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98415,0.0009300302,0.000445378,0.006952418,0.0000938503,0.0001938528,0.001313996,0.00001708264,0.005903376],"genre_scores_gemma":[0.9952443,0.0008419272,0.0007740465,0.0003829037,0.0000237948,0.0001668774,0.0004357451,0.000008134461,0.00212219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0518623,"threshold_uncertainty_score":0.3762891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4801098266926635,"score_gpt":0.4718041578418549,"score_spread":0.008305668850808634,"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."}}