{"id":"W7116755697","doi":"10.1109/tvcg.2025.3646847","title":"Do You “Trust” This Visualization? An Inventory to Measure Trust in Visualizations","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Visualization; Set (abstract data type); Data visualization; Trustworthiness; Consistency (knowledge bases); Reliability (semiconductor); Measure (data warehouse); Information visualization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007509291,0.0006013098,0.0003704883,0.00243394,0.000711512,0.001860882,0.0004792284,0.0006755879,0.001876184],"category_scores_gemma":[0.05002651,0.0004955173,0.0009473076,0.001112764,0.0009798337,0.003056022,0.001830943,0.001032126,0.0004746669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124186,"about_ca_system_score_gemma":0.0007682245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139854,"about_ca_topic_score_gemma":0.001655867,"domain_scores_codex":[0.9946908,0.002256198,0.001092023,0.0002698476,0.001447263,0.0002438663],"domain_scores_gemma":[0.9493902,0.02480858,0.01150532,0.004282547,0.008413176,0.001600118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009387043,0.0006987521,0.68976,0.001572473,0.0005279469,0.0003570071,0.04360232,0.005364412,0.01486579,0.009865046,0.006761317,0.2256862],"study_design_scores_gemma":[0.000188555,0.002245708,0.8316594,0.00118721,0.0006405892,0.001303014,0.03810102,0.0516199,0.01389136,0.02062267,0.03792538,0.0006151134],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9309987,0.0003503931,0.06026945,0.0003583315,0.00004919342,0.0006538731,0.0008754154,0.0003942312,0.006050225],"genre_scores_gemma":[0.9552082,0.00028045,0.04212625,0.00007936811,0.00002010038,0.0007833246,0.0008196171,0.00004688237,0.0006357898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007509291,"threshold_uncertainty_score":0.03971338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02810078117695131,"score_gpt":0.3179430672593482,"score_spread":0.2898422860823969,"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."}}