{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003169528,0.0001084027,0.0001815762,0.0002756382,0.00960197,0.0004388129,0.001531008,0.00008994613,0.00004594537],"category_scores_gemma":[0.001259098,0.0001063265,0.00005142467,0.0003686298,0.0009750418,0.0006059937,0.0004201847,0.0006213628,0.000006313862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004694351,"about_ca_system_score_gemma":0.000961077,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8252464,"about_ca_topic_score_gemma":0.9182153,"domain_scores_codex":[0.9969314,0.0007851619,0.0001953458,0.0001834632,0.001059351,0.000845234],"domain_scores_gemma":[0.9987482,0.0005334712,0.0001132456,0.0003035458,0.000149528,0.000151944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002568016,0.0003832805,0.5440179,0.0002195212,0.00006879402,0.00004654769,0.2222638,0.000007754047,0.0003664659,0.03983939,0.01415272,0.178377],"study_design_scores_gemma":[0.0006769955,0.00009881775,0.5084791,0.0002134043,0.000007844368,6.890262e-8,0.3964679,0.00001675921,0.0002305957,0.001814338,0.09168039,0.0003138364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95429,0.00006209423,0.000003023431,0.003326372,0.0002886656,0.0005361576,0.00001552185,0.00002511593,0.04145308],"genre_scores_gemma":[0.9973112,0.00007250542,0.00001424855,0.00003299314,0.0003278748,0.0001385243,0.00001039922,0.00001452371,0.002077784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1780632,"threshold_uncertainty_score":0.9916874,"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."}}