{"id":"W4366597532","doi":"10.1145/3544549.3583748","title":"Making with Data (and Beyond)","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Agence Nationale de la Recherche; Bpifrance","keywords":"Intersection (aeronautics); Computer science; Visualization; Data science; Representation (politics); Data visualization; External Data Representation; Human–computer interaction; Data mining; Engineering; Artificial intelligence","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":[0.03900648,0.001378256,0.0008689322,0.002804343,0.008821118,0.0204074,0.004129208,0.004159964,0.01494009],"category_scores_gemma":[0.0544021,0.0009168836,0.00215637,0.003800735,0.02734959,0.03629864,0.01947431,0.006961649,0.004025257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002561977,"about_ca_system_score_gemma":0.006459896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003603625,"about_ca_topic_score_gemma":0.004469332,"domain_scores_codex":[0.969102,0.01848504,0.001320825,0.00408808,0.005868781,0.001135255],"domain_scores_gemma":[0.9525566,0.02352854,0.001207846,0.01697662,0.004347892,0.001382513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003963762,0.00006336224,0.00138901,0.0005021612,0.00002861494,0.0002876424,0.06640224,0.0007407818,0.001988307,0.7560679,0.03201562,0.1404748],"study_design_scores_gemma":[0.000009677743,0.00004533858,0.0003779003,0.0008248261,0.00002191114,0.0004840808,0.0199956,0.001069193,0.002238027,0.1763379,0.798542,0.00005357992],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01593128,0.004753008,0.7368301,0.07613314,0.003999851,0.0005803703,0.0004874436,0.001588334,0.1596965],"genre_scores_gemma":[0.3357369,0.01157632,0.5346425,0.01762868,0.002397814,0.001403717,0.001448293,0.002635127,0.09253067],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03900648,"threshold_uncertainty_score":0.2062885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066343685810943,"score_gpt":0.3666246985949885,"score_spread":0.2599903300138942,"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."}}