{"id":"W4409365255","doi":"10.1609/aaai.v39i13.33509","title":"Verifying Proportionality in Temporal Voting","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Engineering and Physical Sciences Research Council; Ministry of Education, India; Alan Turing Institute","keywords":"Proportionality (law); Voting; Computer science; Political science; Law","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":[],"consensus_categories":[],"category_scores_codex":[0.001014947,0.0001858983,0.0002864196,0.0002143948,0.0001587289,0.0002356137,0.001666582,0.00008578355,0.00003170811],"category_scores_gemma":[0.0004451418,0.00014193,0.0001460273,0.001107439,0.0001581318,0.0003377276,0.000399196,0.0003829814,0.00002088838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007797556,"about_ca_system_score_gemma":0.0001462525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007056522,"about_ca_topic_score_gemma":0.00009440999,"domain_scores_codex":[0.9979509,0.00002494151,0.0007860461,0.0004996594,0.0004160018,0.000322504],"domain_scores_gemma":[0.9988973,0.00007243334,0.0003671776,0.0001526073,0.0004697965,0.0000407452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001702232,0.0001029765,0.002797749,0.00003971761,0.00001311697,6.31027e-7,0.0006403152,0.0004500873,0.001389775,0.9772596,0.00005079695,0.01723826],"study_design_scores_gemma":[0.0000340999,0.00005247059,0.001435145,0.0006151129,0.00001119736,0.000001711889,0.0006235843,0.9124558,0.05984099,0.02462904,0.00008632142,0.0002145302],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7225738,0.00005035748,0.2500168,0.006662087,0.0008144016,0.0005695897,0.000001990947,0.0001548389,0.01915618],"genre_scores_gemma":[0.9974365,0.000004057928,0.00201194,0.0002173708,0.00003431831,0.0000149487,4.062234e-7,0.000005140115,0.0002752915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9526305,"threshold_uncertainty_score":0.5787739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05991866952502942,"score_gpt":0.3073288252519741,"score_spread":0.2474101557269447,"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."}}