{"id":"W4417248863","doi":"10.1109/tvcg.2025.3634777","title":"Correcting Misperceptions at a Glance: Using Data Visualizations to Reduce Political Sectarianism","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Sectarianism; Politics; Psychological intervention; Motivated reasoning; Survey data collection; Control (management); Political violence; Range (aeronautics)","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"],"consensus_categories":[],"category_scores_codex":[0.0002928201,0.0002879986,0.0002781145,0.0008655973,0.001061327,0.0005164545,0.0008767965,0.000151813,0.00003360185],"category_scores_gemma":[0.00002490727,0.0003187375,0.00007385618,0.002633143,0.0001010468,0.0006373737,0.00009332369,0.000190612,0.00002042773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307503,"about_ca_system_score_gemma":0.0001768685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008485922,"about_ca_topic_score_gemma":0.0001223116,"domain_scores_codex":[0.9975808,0.0001937659,0.0005246697,0.0009062942,0.0003610201,0.0004334072],"domain_scores_gemma":[0.9981193,0.0001367167,0.00009416112,0.001081339,0.0002678734,0.0003005915],"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":[0.00001330475,0.000319206,0.0001265501,0.00004476895,0.00007400537,0.000003247271,0.0006114518,0.002244298,0.0001365216,0.9903219,0.001858993,0.004245719],"study_design_scores_gemma":[0.0004688117,0.00007686797,0.0002315765,0.0001304628,0.00006250478,0.00004472169,0.00009017635,0.9936743,0.001002415,0.0007089725,0.00318287,0.0003263462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006253882,0.00001762831,0.9910762,0.0003614806,0.001324814,0.0003184309,0.000108556,0.0003836202,0.0001553477],"genre_scores_gemma":[0.9727169,0.00007280993,0.01514302,0.0110139,0.0001427767,0.00002205262,0.0001984567,0.0000415972,0.0006484287],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.99143,"threshold_uncertainty_score":0.9999264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05880523301195154,"score_gpt":0.3684059441336142,"score_spread":0.3096007111216627,"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."}}