{"id":"W4240175495","doi":"10.3138/9781442665828-013","title":"10. What Accounts For The Local Diversity Gap? Supply And Demand Of Visible Minority Candidates In Ontario Municipal Politics","year":2016,"lang":"en","type":"book-chapter","venue":"University of Toronto Press eBooks","topic":"Canadian Identity and History","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Diversity (politics); Politics; Geography; Political science; Business; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006823393,0.0001195652,0.0002513039,0.001214901,0.0106429,0.005427167,0.0008933401,0.001357481,0.03058756],"category_scores_gemma":[0.003264418,0.000260782,0.0002286882,0.003138217,0.003448923,0.001741902,0.002200381,0.0008799247,0.001306478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04092064,"about_ca_system_score_gemma":0.0314924,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9240713,"about_ca_topic_score_gemma":0.9858893,"domain_scores_codex":[0.9986048,0.0001694879,0.00002435317,0.00006422769,0.0003244715,0.0008126039],"domain_scores_gemma":[0.9978262,0.0004508735,0.0002455969,0.0000581554,0.0004358512,0.0009832864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000297061,0.0001488038,0.2176618,0.0004432792,0.00002710623,0.001198102,0.3189147,0.0004309478,0.001459607,0.2459968,0.1368283,0.07659347],"study_design_scores_gemma":[0.00003106152,0.00003992283,0.3301059,0.0004415716,0.00002732025,0.000169097,0.3679309,0.0006196707,0.0002375963,0.0103819,0.289976,0.00003904066],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6207945,0.001621997,0.0003193347,0.0406766,0.00008032451,0.00004731036,0.001913799,0.00002039736,0.3345259],"genre_scores_gemma":[0.9559507,0.0005623381,0.00008677771,0.0008438354,0.00003754525,0.00002327088,0.0001682498,0.00001705973,0.0423103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07592875,"threshold_uncertainty_score":0.2969015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0230465942122586,"score_gpt":0.2080378155955413,"score_spread":0.1849912213832827,"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."}}