{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003064115,0.0003828122,0.0002916639,0.001076164,0.0007558108,0.002083575,0.0005797386,0.0002651828,0.00003766501],"category_scores_gemma":[0.00002274445,0.0003913818,0.0001207618,0.002073256,0.0002093288,0.00390924,0.00001809993,0.0003279582,0.00002961987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002217727,"about_ca_system_score_gemma":0.00009867761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001472526,"about_ca_topic_score_gemma":0.00006139979,"domain_scores_codex":[0.9977759,0.0001398495,0.0005706666,0.0005366821,0.0006250213,0.0003518392],"domain_scores_gemma":[0.9980662,0.0001252432,0.0003154486,0.0006301317,0.0005839462,0.0002790206],"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":[0.000006021706,0.0001689803,0.0001225436,0.00004323874,0.00004030999,0.000001202909,0.0006510151,0.0001503162,0.0000763858,0.9848283,0.0003999916,0.01351172],"study_design_scores_gemma":[0.0007758422,0.0001651482,0.0001997805,0.00002951863,0.00003727332,0.00004252974,0.000103093,0.970668,0.002728531,0.001344247,0.02341588,0.0004901981],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002984443,0.00000995581,0.9937257,0.000262682,0.001744784,0.0003471243,0.00005355387,0.0007133778,0.0001584138],"genre_scores_gemma":[0.9905159,0.000217069,0.004875968,0.003831345,0.0001361582,0.00003894437,0.0002130867,0.00003752769,0.0001340179],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9888497,"threshold_uncertainty_score":0.9998538,"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."}}