{"id":"W4231111369","doi":"10.2139/ssrn.3592180","title":"Ties that Bind (and Social Distance): How Social Capital Helps Communities Weather the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Social Capital and Networks","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Social capital; Social distance; Demographic economics; Pandemic; Coronavirus disease 2019 (COVID-19); Distribution (mathematics); Outbreak; Capital (architecture); Social mobility; Development economics; Business; Geography; Economics; Political science; Biology; Virology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001964929,0.0002043357,0.0002718407,0.00003228431,0.005774477,0.0004349614,0.000568663,0.0002155933,0.0001074452],"category_scores_gemma":[0.00008675295,0.0001546498,0.0002000335,0.0002051943,0.001333185,0.0003246497,0.0001017094,0.00227673,0.00001080915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008904336,"about_ca_system_score_gemma":0.001966488,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001291416,"about_ca_topic_score_gemma":0.03849601,"domain_scores_codex":[0.9965118,0.0007884885,0.0001716587,0.0001572362,0.0005764341,0.001794425],"domain_scores_gemma":[0.9991954,0.0002792095,0.0002029724,0.00006358714,0.00006352191,0.0001953012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001161714,0.00002234072,0.01224043,0.000009764554,0.0001747055,0.000003088777,0.609676,0.000001232,0.00001138972,0.3676159,0.003696558,0.006432374],"study_design_scores_gemma":[0.0005436072,0.0001070569,0.000617464,0.000003198949,0.00005421995,0.00002716485,0.7706934,0.000007237643,9.48611e-7,0.1442019,0.08350839,0.0002353274],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8371294,0.008946778,0.0008730962,0.1501991,0.0003366391,0.0002515002,0.00002039504,0.0001245286,0.002118583],"genre_scores_gemma":[0.984195,0.007478628,0.000002443802,0.003248462,0.00333413,0.0000109746,0.00000594836,0.00002586565,0.001698525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.223414,"threshold_uncertainty_score":0.9955199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06220888149187922,"score_gpt":0.2935287891772687,"score_spread":0.2313199076853894,"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."}}