{"id":"W4400375525","doi":"10.48550/arxiv.2407.02611","title":"Co-Designing Unstructured Text Data Visualization Systems","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Unstructured data; Computer science; Visualization; Data visualization; Information visualization; Data science; Data mining; Big data","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01770261,0.002239964,0.001273308,0.003943496,0.001878164,0.009634557,0.004556923,0.002718351,0.01008117],"category_scores_gemma":[0.06580343,0.001302418,0.001780144,0.002748326,0.001963232,0.01141118,0.008430279,0.002700384,0.003526705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136094,"about_ca_system_score_gemma":0.001748552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00122037,"about_ca_topic_score_gemma":0.002091056,"domain_scores_codex":[0.9876736,0.00735691,0.0008378659,0.001627328,0.002091698,0.0004127394],"domain_scores_gemma":[0.9379563,0.03495509,0.002191071,0.01104386,0.01171091,0.00214284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001588067,0.001302454,0.00954941,0.005148854,0.0005415039,0.002173894,0.05456479,0.03979136,0.09849043,0.05616642,0.05217506,0.6785077],"study_design_scores_gemma":[0.0004346516,0.0008886875,0.003094288,0.001241788,0.0003094063,0.001471622,0.01503032,0.48992,0.1055701,0.09481744,0.2867425,0.0004791763],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01368422,0.0002224363,0.9712279,0.000793738,0.0001334574,0.0005404394,0.0003595608,0.01018554,0.002852703],"genre_scores_gemma":[0.06341233,0.0002251759,0.9298078,0.0001272246,0.00005366707,0.0007429836,0.0009524922,0.002268763,0.002409527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01770261,"threshold_uncertainty_score":0.09362149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1273281141633771,"score_gpt":0.2607642495659284,"score_spread":0.1334361354025513,"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."}}