{"id":"W4415140567","doi":"10.1016/j.ins.2025.122763","title":"Hesitant fuzzy linguistic term set based preference representation for composite decision makers in the graph model for conflict resolution","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for International Governance Innovation; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Preference; Fuzzy logic; Term (time); Semantics (computer science); Group decision-making; Conflict resolution; Fuzzy set; Representation (politics); Set (abstract data type)","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0107612,0.0001887127,0.0002907379,0.00149485,0.0008483218,0.001710195,0.001656773,0.0001043431,0.00001164748],"category_scores_gemma":[0.01390402,0.0001216982,0.0001627571,0.002461602,0.0002687526,0.001888208,0.0001071918,0.0001129729,0.0000219201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008093142,"about_ca_system_score_gemma":0.0003018052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003766093,"about_ca_topic_score_gemma":0.00008435984,"domain_scores_codex":[0.9954189,0.0002036673,0.001603318,0.0004866698,0.001913099,0.000374313],"domain_scores_gemma":[0.9885304,0.009112798,0.0006182873,0.0006428032,0.00103956,0.00005614371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002361769,0.0001417384,0.01178036,0.00009895672,0.00001721176,0.000001459807,0.02107826,0.6543472,0.001548341,0.07106802,0.02231481,0.2152418],"study_design_scores_gemma":[0.001087547,0.00007268455,0.01312417,0.0001049323,0.00000969859,0.000001437232,0.001372364,0.8854197,0.0001917033,0.09447625,0.004004081,0.0001354815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09645753,0.00003494803,0.8973843,0.001114577,0.0006000901,0.001656731,0.0001315159,0.00003880021,0.002581473],"genre_scores_gemma":[0.9397309,0.000005048833,0.05817045,0.00168595,0.00002729581,0.0002677885,0.00004402844,0.000003557037,0.000064992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8432733,"threshold_uncertainty_score":0.9993261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3729088248077768,"score_gpt":0.4874063125129494,"score_spread":0.1144974877051726,"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."}}