{"id":"W3173208890","doi":"","title":"Cities in a Post-COVID World","year":2021,"lang":"en","type":"preprint","venue":"London School of Economics and Political Science Research Online (London School of Economics and Political Science)","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Metropolitan area; Timeline; Pandemic; Economic geography; Geography; Context (archaeology); Coronavirus disease 2019 (COVID-19); Scale (ratio); Inequality; Politics; Urban geography; Economic growth; Regional science; Development economics; Urban planning; Political science; Cartography; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000369273,0.0001707808,0.0002007343,0.0009496269,0.001189942,0.003671708,0.000383824,0.0008888185,0.005307899],"category_scores_gemma":[0.001824655,0.0001796095,0.0002609091,0.002879109,0.001522539,0.003868422,0.002575286,0.001202349,0.0004230917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002369557,"about_ca_system_score_gemma":0.0008701693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04251385,"about_ca_topic_score_gemma":0.07116385,"domain_scores_codex":[0.9996158,0.000114841,0.00001257009,0.00005468005,0.00006227868,0.0001398655],"domain_scores_gemma":[0.9992034,0.0001044935,0.0002898225,0.00005149111,0.0001147744,0.0002360216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005034623,0.0005355471,0.5254275,0.0002512921,0.0003402012,0.002561155,0.01131255,0.02712346,0.0008961219,0.338144,0.0359851,0.05691976],"study_design_scores_gemma":[0.0001098209,0.000332586,0.7365758,0.000188396,0.0001017784,0.0005442936,0.03907097,0.01280851,0.0005474149,0.05880998,0.1507781,0.0001324187],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9453092,0.001494293,0.0008394858,0.01367221,0.0001298433,0.00002217171,0.001771011,0.00002016972,0.03674162],"genre_scores_gemma":[0.9950235,0.0008551484,0.0001708907,0.0006307066,0.0000520131,0.00001504991,0.0006015639,0.00000662034,0.002644711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04251385,"threshold_uncertainty_score":0.0845328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09781827154817133,"score_gpt":0.3750263239241216,"score_spread":0.2772080523759503,"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."}}