{"id":"W346574303","doi":"10.3102/10769986030004353","title":"No Humble Pie: The Origins and Usage of a Statistical Chart","year":2005,"lang":"en","type":"article","venue":"Journal of Educational and Behavioral Statistics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chart; Pie chart; Appeal; Brother; Plot (graphics); History; Mathematics; Law; Statistics; Political science","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":[0.02286728,0.0008164385,0.0008447196,0.004755121,0.004470534,0.01357301,0.001873768,0.003241984,0.005319961],"category_scores_gemma":[0.1627503,0.0006267175,0.0005617901,0.006441672,0.02460124,0.01359185,0.003396478,0.007020762,0.00143259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003135103,"about_ca_system_score_gemma":0.004699576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006564342,"about_ca_topic_score_gemma":0.006607598,"domain_scores_codex":[0.9762217,0.01678538,0.001122106,0.001417585,0.00398768,0.0004655734],"domain_scores_gemma":[0.9071105,0.07603705,0.002472481,0.005217673,0.008090166,0.001072054],"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":[0.00003884559,0.000004319075,0.0003423346,0.00005718193,0.000005506987,0.00006054685,0.003583995,0.0002760304,0.00006235288,0.9356617,0.0256722,0.03423494],"study_design_scores_gemma":[0.00001172628,0.00002358254,0.0005612014,0.0005071744,0.00001057242,0.000223664,0.001831015,0.001876299,0.0004160353,0.64867,0.3457945,0.00007437781],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01460605,0.02805935,0.6217366,0.1613906,0.01491161,0.0001529796,0.0009375518,0.002172032,0.1560332],"genre_scores_gemma":[0.4912896,0.02564791,0.3805388,0.03310618,0.01347924,0.0006860345,0.0007794122,0.004896711,0.04957599],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02286728,"threshold_uncertainty_score":0.1209351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04239665616900781,"score_gpt":0.3786457753845167,"score_spread":0.3362491192155089,"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."}}