{"id":"W2252875879","doi":"10.5167/uzh-87954","title":"Metropolitan Geography, Electoral Participation, and Partisan Competition","year":2013,"lang":"en","type":"article","venue":"Zurich Open Repository and Archive (University of Zurich)","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Metropolitan area; Dominance (genetics); Voting; Electoral geography; Competition (biology); Political science; Economic geography; Geography; Survey data collection; Political economy; Inequality; Regional science; Economic growth; Politics; Sociology; 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":[0.001819309,0.0001341648,0.0004326317,0.002177632,0.001161575,0.00198131,0.0003861832,0.0002112973,0.008084108],"category_scores_gemma":[0.005958549,0.0001608185,0.0002102542,0.004618194,0.001071233,0.000879849,0.002124541,0.0002972865,0.0006802548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006222987,"about_ca_system_score_gemma":0.0003808345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01349213,"about_ca_topic_score_gemma":0.03795993,"domain_scores_codex":[0.9981006,0.00089772,0.00008585245,0.0002170383,0.0002223427,0.0004763973],"domain_scores_gemma":[0.9965901,0.001104713,0.001063333,0.0003337437,0.0003154023,0.0005927215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002180127,0.00007750668,0.9644237,0.00008429366,0.0001746052,0.0001388902,0.00823842,0.0007737736,0.0003385111,0.00798195,0.001551126,0.01599932],"study_design_scores_gemma":[0.000007520489,0.00003459989,0.9838319,0.00002063896,0.00002001759,0.00004038068,0.009984099,0.0003627558,0.00007226149,0.00117107,0.004448026,0.000006810485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937651,0.0001425456,0.000116798,0.0001127222,0.000002395539,0.000006015817,0.0003205148,0.000003557557,0.005530394],"genre_scores_gemma":[0.9988306,0.00006229823,0.00003601016,0.000007481839,0.000004307323,0.000004870276,0.0003389937,0.000002522188,0.0007129455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01349213,"threshold_uncertainty_score":0.02704406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01853047761903895,"score_gpt":0.2726280537062478,"score_spread":0.2540975760872089,"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."}}