{"id":"W2942658839","doi":"10.1109/mcg.2019.2914844","title":"Broadening Intellectual Diversity in Visualization Research Papers","year":2019,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Visualization; Computer science; Diversity (politics); Data science; Information visualization; Data visualization; Creative visualization; Human–computer interaction; Artificial intelligence; Sociology","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1919262,0.002089251,0.002646125,0.02766568,0.01056254,0.04606766,0.004652963,0.005126824,0.02139988],"category_scores_gemma":[0.4638357,0.001425071,0.002933518,0.022755,0.006531863,0.03754762,0.03576107,0.006366505,0.008289536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004685205,"about_ca_system_score_gemma":0.01402785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004083145,"about_ca_topic_score_gemma":0.0008456632,"domain_scores_codex":[0.7918642,0.12377,0.0164594,0.01140212,0.05083388,0.005670402],"domain_scores_gemma":[0.4166047,0.2715449,0.04579189,0.1062205,0.1201984,0.03963973],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005965641,0.0003558997,0.01529459,0.004526993,0.0004249925,0.0008669128,0.02908641,0.001396733,0.009474888,0.120555,0.06225576,0.7551653],"study_design_scores_gemma":[0.0003362619,0.0004408517,0.01431564,0.005754709,0.0004308113,0.001909936,0.01785737,0.004080674,0.007359974,0.4089105,0.538152,0.0004512849],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1110973,0.03713247,0.3913204,0.1739449,0.03955746,0.005027562,0.001839891,0.00639306,0.2336868],"genre_scores_gemma":[0.4914052,0.01922259,0.3623076,0.01692479,0.03011327,0.006866037,0.002335204,0.005580721,0.06524464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8080738,"threshold_uncertainty_score":0.9964989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05874436117135488,"score_gpt":0.3412487979354425,"score_spread":0.2825044367640877,"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."}}