{"id":"W2156440763","doi":"10.1109/tvcg.2010.164","title":"How Information Visualization Novices Construct Visualizations","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":270,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Visualization; Computer science; Visual analytics; Data visualization; Information visualization; Construct (python library); Human–computer interaction; Process (computing); Interactive visual analysis; Heuristics; Software visualization; Bar chart; Data science; Data mining; Software; Software development; Programming language; Component-based 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01008934,0.001098694,0.000822218,0.003280388,0.001079554,0.01799037,0.00319531,0.003255517,0.006319672],"category_scores_gemma":[0.09162673,0.001214697,0.001243827,0.001393063,0.002652609,0.01174603,0.004077464,0.003430818,0.002473231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320813,"about_ca_system_score_gemma":0.001931609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00315736,"about_ca_topic_score_gemma":0.002616627,"domain_scores_codex":[0.9912171,0.003489122,0.0005798219,0.001747109,0.002095803,0.0008710452],"domain_scores_gemma":[0.9432489,0.03716449,0.003659649,0.005424324,0.007302945,0.003199689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0006610849,0.001684286,0.1135444,0.002136867,0.0002612011,0.003799754,0.4561143,0.009380464,0.04089664,0.02138971,0.0251878,0.3249434],"study_design_scores_gemma":[0.0005118961,0.002791804,0.1072496,0.004707533,0.0005690996,0.006235047,0.3891415,0.06804939,0.03805236,0.09662737,0.2843885,0.001675932],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9223419,0.001361219,0.05344286,0.002509408,0.000104596,0.0003681153,0.0003502723,0.001601706,0.01791993],"genre_scores_gemma":[0.9284718,0.001797723,0.0576161,0.0006161614,0.00003259926,0.0002220365,0.0009057206,0.0004869889,0.009850811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01799037,"threshold_uncertainty_score":0.05335814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267559004537154,"score_gpt":0.2681414677359401,"score_spread":0.2554658776905686,"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."}}