{"id":"W4225771412","doi":"10.1145/3512896","title":"Abstractions for Visualizing Preferences in Group Decisions","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Situational ethics; Set (abstract data type); Visualization; Preference; Group decision-making; Stakeholder; Task (project management); Preference elicitation; Human–computer interaction; Data science; Order (exchange); Knowledge management; Decision support system; Management science; Decision analysis; Artificial intelligence; Psychology; Engineering; Systems engineering","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":[0.0004233089,0.0001223633,0.0001534471,0.0003415836,0.0004642198,0.000214954,0.002421857,0.00002837237,0.00003248819],"category_scores_gemma":[0.0002081894,0.0001033369,0.0001165722,0.0004644326,0.0000225364,0.0008274674,0.001370229,0.0002500901,0.000004908195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001277795,"about_ca_system_score_gemma":0.00001691261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002382055,"about_ca_topic_score_gemma":0.00001158132,"domain_scores_codex":[0.9986885,0.00001898205,0.0004069636,0.0003558043,0.0003576028,0.0001722159],"domain_scores_gemma":[0.998827,0.0002556466,0.0003656256,0.0003821011,0.0001363117,0.00003328139],"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":[0.0001399577,0.002563456,0.004465522,0.0001382588,0.0001261035,0.000001314696,0.00601335,0.00641268,0.02497426,0.7932031,0.07839687,0.0835651],"study_design_scores_gemma":[0.002778962,0.002285796,0.03558936,0.0007018622,0.00006931041,0.00007761632,0.003157721,0.6170163,0.02127973,0.2168895,0.09904896,0.00110492],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.88621,0.00001736074,0.1030654,0.003163096,0.003444026,0.001046507,0.0000380568,0.0002586638,0.002756863],"genre_scores_gemma":[0.9862558,0.000004555694,0.01296304,0.0004122152,0.0001061,0.00009304328,0.00001124318,0.00001100795,0.0001430326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6106036,"threshold_uncertainty_score":0.4500452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185662284473608,"score_gpt":0.3877788652634,"score_spread":0.2692126368160392,"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."}}