{"id":"W4402401820","doi":"10.1109/tvcg.2024.3456361","title":"Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data visualization; Visualization; Data modeling; Human–computer interaction; Data science; Data mining; Software 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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007207437,0.0002539607,0.0002465207,0.001079805,0.0003384648,0.001374473,0.001046264,0.0001075199,0.00001831324],"category_scores_gemma":[0.0000305765,0.000258928,0.0000259134,0.002057327,0.00008033603,0.001993845,0.0001192049,0.0001882522,0.00001634413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002323292,"about_ca_system_score_gemma":0.0001216922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003097641,"about_ca_topic_score_gemma":0.0001206182,"domain_scores_codex":[0.9975685,0.000220837,0.0005170607,0.00104998,0.000376293,0.0002672939],"domain_scores_gemma":[0.998158,0.0003402085,0.00006781391,0.00113943,0.0001138804,0.0001806299],"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":[0.00003107015,0.0004432983,0.001124586,0.0002603871,0.0001103046,0.00003154084,0.001287975,0.0004935903,0.00007254507,0.9669132,0.002155914,0.02707561],"study_design_scores_gemma":[0.0002328019,0.00007455846,0.001864126,0.0002392831,0.00003016948,0.00002767614,0.00003110737,0.9848839,0.00006509598,0.0002179003,0.01203211,0.0003012809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003372549,0.0001912327,0.9943197,0.0003373782,0.000724756,0.000241757,0.0005066413,0.0002949472,0.00001102291],"genre_scores_gemma":[0.9823149,0.002083683,0.01102304,0.002888022,0.0001389826,0.00002129831,0.001186736,0.00006313003,0.0002802178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9843903,"threshold_uncertainty_score":0.9999863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1206675430272184,"score_gpt":0.3778552767480552,"score_spread":0.2571877337208369,"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."}}