{"id":"W4235463506","doi":"10.1145/3099023.3099055","title":"Impact of Individual Differences on User Experience with a Visualization Interface for Public Engagement","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Visualization; User engagement; Human–computer interaction; Computer science; Information visualization; Cognition; Eye tracking; Task (project management); User experience design; User interface; Public engagement; User interface design; Cognitive psychology; Psychology; World Wide Web; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003429858,0.0003769173,0.0003727647,0.00056654,0.0003488649,0.001829168,0.0003214261,0.0005224168,0.005747932],"category_scores_gemma":[0.04627121,0.0001646731,0.0003976594,0.0002766112,0.0004743403,0.0008805743,0.001288002,0.000398959,0.000444117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001900529,"about_ca_system_score_gemma":0.0001669094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005887953,"about_ca_topic_score_gemma":0.0005981486,"domain_scores_codex":[0.9971095,0.001631448,0.0002537154,0.0003412698,0.0004629996,0.0002012323],"domain_scores_gemma":[0.9580385,0.03596871,0.001528036,0.00198515,0.001275947,0.001203658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01020948,0.002935143,0.4005094,0.002236984,0.0008225095,0.001490067,0.07874339,0.006769549,0.1726379,0.001732639,0.003812124,0.3181009],"study_design_scores_gemma":[0.0003158577,0.004926077,0.927864,0.0002841054,0.0006042987,0.001230259,0.01864294,0.01290396,0.02168783,0.002546844,0.008604784,0.0003889487],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941399,0.0001556493,0.003022084,0.0000699554,0.00001312381,0.00004472708,0.00005882505,0.0001288801,0.002366791],"genre_scores_gemma":[0.9968591,0.000067627,0.002469136,0.00002783815,0.000007876787,0.00005096295,0.00005629304,0.00004372466,0.0004174379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005747932,"threshold_uncertainty_score":0.01922882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1266948298683887,"score_gpt":0.4094632521562772,"score_spread":0.2827684222878885,"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."}}