{"id":"W7001842327","doi":"","title":"Making Every Vote Count: Reassessing Canada's Electoral System","year":2023,"lang":"en","type":"article","venue":"Project Muse (Johns Hopkins University)","topic":"Political Systems and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Politics; Electoral system; Voting; Work (physics)","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":[0.01074295,0.000256099,0.0004033885,0.00310967,0.02089857,0.01037083,0.002800282,0.001813572,0.0047806],"category_scores_gemma":[0.04447138,0.0003382706,0.000255615,0.005290146,0.00478571,0.002424783,0.003243587,0.0030329,0.0004380393],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1124184,"about_ca_system_score_gemma":0.2144587,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950811,"about_ca_topic_score_gemma":0.9987962,"domain_scores_codex":[0.9926422,0.002018749,0.0002031423,0.0005800492,0.002423916,0.00213187],"domain_scores_gemma":[0.9661747,0.00554478,0.001046186,0.001368129,0.02162042,0.004245741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004152604,0.0001378166,0.3073161,0.0002134885,0.0001321739,0.0003834465,0.08766594,0.003651837,0.001055794,0.06585969,0.2563036,0.2768649],"study_design_scores_gemma":[0.00007359746,0.0001324645,0.3828645,0.0004349996,0.0001753458,0.0001163892,0.1912025,0.005845472,0.00201553,0.009886465,0.4069488,0.0003039141],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7519649,0.002443681,0.002295186,0.1146536,0.001085669,0.0002026188,0.003413059,0.0002227794,0.1237185],"genre_scores_gemma":[0.9788249,0.0004276958,0.002317422,0.002815704,0.00005250936,0.00004321754,0.0005095287,0.00008181585,0.01492718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8875816,"threshold_uncertainty_score":0.8156561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03507956106827834,"score_gpt":0.2703772729790742,"score_spread":0.2352977119107959,"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."}}