{"id":"W4230643689","doi":"10.4018/978-1-4666-7456-1.ch004","title":"Type-One Fuzzy Logic for Quantitatively Defining Imprecise Linguistic Terms in Politics and Public Policy","year":2015,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fuzzy logic; Set (abstract data type); Presidential system; Fuzzy set; Class (philosophy); Politics; Mathematical economics; Ask price; Type-2 fuzzy sets and systems; Moderation; Mathematics; Artificial intelligence; Computer science; Fuzzy number; Political science; Economics; Law; Statistics; Economy","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":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002544607,0.0006789004,0.001308436,0.001128307,0.0001968073,0.001060085,0.001277039,0.0006716217,0.00005017123],"category_scores_gemma":[0.02899749,0.0005928045,0.0002400552,0.0002119794,0.0003931362,0.0001495676,0.0008125447,0.0004273707,0.0003872485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005875997,"about_ca_system_score_gemma":0.001159363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002341661,"about_ca_topic_score_gemma":0.0003961909,"domain_scores_codex":[0.9939795,0.0001328832,0.001746152,0.0013754,0.001839315,0.0009267082],"domain_scores_gemma":[0.9934126,0.002251766,0.0008799473,0.001068319,0.001795523,0.0005919149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009489746,0.00001935913,0.000181278,0.00002057172,0.0000274229,0.00005071767,0.0002489536,0.000003296015,0.00001291731,0.9694995,0.00145122,0.02838983],"study_design_scores_gemma":[0.00100802,0.0002268397,0.000208622,0.0002640195,0.00003000633,0.00005172792,0.0001175954,0.0003562885,0.000001718103,0.9447808,0.05237534,0.0005790393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001538845,0.001573785,0.001935087,0.0002572708,0.001227427,0.000904067,0.00106434,0.0001009065,0.9913983],"genre_scores_gemma":[0.9046699,0.00001981999,0.03735641,0.001254651,0.001043167,0.0000496891,0.00002683382,0.0001672409,0.05541231],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.935986,"threshold_uncertainty_score":0.9999769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3114031335719822,"score_gpt":0.4502623175291929,"score_spread":0.1388591839572107,"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."}}