{"id":"W4311111855","doi":"10.1080/02723638.2022.2151753","title":"Visualizing superdiversity and “seeing” urban socio-economic complexity","year":2022,"lang":"en","type":"article","venue":"Urban Geography","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Diversity (politics); Sociology; Urban studies; Suite; Diversification (marketing strategy); Data science; Economic geography; Geography; Computer science; Political science; Anthropology","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064673,0.0001546665,0.0002475578,0.0002263489,0.004916512,0.0001098199,0.0002853529,0.00005157586,0.001148785],"category_scores_gemma":[0.00002849352,0.0001815712,0.0001806887,0.0003247456,0.0008694208,0.0002314502,0.0003390258,0.0002022841,0.00002603849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467273,"about_ca_system_score_gemma":0.00007381486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006201034,"about_ca_topic_score_gemma":0.0006483717,"domain_scores_codex":[0.9983259,0.0003148557,0.0001948792,0.0003803737,0.0003647948,0.0004192283],"domain_scores_gemma":[0.999357,0.0001766139,0.0001022955,0.0001637364,0.00004714791,0.0001532352],"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":[0.00001237698,0.00004065777,0.8938415,0.000004923508,0.00008269965,0.000002909059,0.02969711,0.000003227229,0.000003531298,0.03579769,0.04019526,0.0003181356],"study_design_scores_gemma":[0.0006046606,0.00009933936,0.2783808,0.000004199951,0.00006489944,0.00000130188,0.04956616,0.00009815959,0.000004005016,0.004839137,0.6659284,0.000408937],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411669,0.003466697,0.00006075437,0.001681129,0.0008636388,0.0003675444,0.000171875,0.0002951721,0.05192631],"genre_scores_gemma":[0.9980037,0.0001584166,0.0001051416,0.0004458719,0.0002720431,0.00002154045,0.00002153091,0.00001139336,0.0009603676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6257331,"threshold_uncertainty_score":0.9997643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03637432595170696,"score_gpt":0.2792999153127401,"score_spread":0.2429255893610331,"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."}}