{"id":"W1727200793","doi":"","title":"Choosing the Who, the What, and the How: Maximizing Accountability and Representation through European Electoral Systems","year":2012,"lang":"en","type":"article","venue":"The Journal of Macrodynamic Analysis (Memorial University of Newfoundland)","topic":"European Union Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Representation (politics); Accountability; Maximization; Proportional representation; Law and economics; Computer science; Political science; Mathematical economics; Microeconomics; Economics; Law; Politics; Democracy","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":[0.006704543,0.0001107115,0.0003160421,0.00005020219,0.001566682,0.0003116093,0.0006909182,0.00003455401,0.00001406702],"category_scores_gemma":[0.000262225,0.0000533556,0.0001938618,0.0005770336,0.001431362,0.001136053,0.0001529523,0.000311005,0.00000114661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001106999,"about_ca_system_score_gemma":0.00005193334,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03071129,"about_ca_topic_score_gemma":0.01254973,"domain_scores_codex":[0.9954224,0.003493964,0.0002508086,0.0001044917,0.000500433,0.0002279477],"domain_scores_gemma":[0.9974127,0.001149096,0.0008923303,0.0002986049,0.0001873817,0.00005985087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.005324991,0.000213505,0.22632,0.00007929796,0.006730447,0.00002056452,0.6372159,0.007949569,0.0005646031,0.1024238,0.003847501,0.009309858],"study_design_scores_gemma":[0.004424205,0.0001029223,0.6205585,0.0001546571,0.007387917,0.00009513202,0.3068701,0.005077628,0.000005933634,0.001626654,0.05330463,0.0003916897],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98092,0.002494889,0.00283262,0.009013423,0.0006308952,0.000213224,0.000005514287,0.000007720504,0.003881675],"genre_scores_gemma":[0.9935939,0.004661036,0.0000197903,0.0001184056,0.0009022628,4.195778e-8,8.186729e-7,0.000005981243,0.0006977381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3942386,"threshold_uncertainty_score":0.9997332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378526128196713,"score_gpt":0.2697621964086281,"score_spread":0.245976935126661,"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."}}