{"id":"W4416035532","doi":"10.18653/v1/2025.emnlp-main.1472","title":"From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text","year":2025,"lang":"","type":"article","venue":"","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Centre International de Recherche sur le Cancer","keywords":"Natural (archaeology); Empirical research; Government (linguistics); Natural language; Identification (biology)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001147292,0.000263969,0.0005407216,0.0004753853,0.0005330127,0.0003736969,0.0002702757,0.0001023651,0.00197687],"category_scores_gemma":[0.0005843185,0.0002789048,0.0001247141,0.001116539,0.0001403744,0.0003564356,0.0003263683,0.0001554794,0.0001667618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000288431,"about_ca_system_score_gemma":0.0003666284,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0203634,"about_ca_topic_score_gemma":0.02468358,"domain_scores_codex":[0.9973623,0.0004570376,0.0006579042,0.0008099864,0.0002278773,0.000484851],"domain_scores_gemma":[0.9975326,0.001806007,0.0001178867,0.0001662158,0.00006773414,0.0003095483],"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":[0.0001166757,0.0002268391,0.07774884,0.00006835126,0.0005226947,0.00001940477,0.4103751,0.05334941,0.003369474,0.03626321,0.002645377,0.4152946],"study_design_scores_gemma":[0.001773814,0.0002817359,0.2608632,0.002567272,0.0001558716,7.859551e-7,0.4586626,0.07553866,0.002721791,0.1370946,0.05821351,0.002126195],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9660329,0.0003823187,0.00519269,0.01166123,0.0006292285,0.0003896886,0.0000287761,0.00003408032,0.01564909],"genre_scores_gemma":[0.984775,0.00005209629,0.009344577,0.001604837,0.000406057,0.00003403049,0.000007019428,0.000009670377,0.003766691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4131684,"threshold_uncertainty_score":0.9999663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03350814474349846,"score_gpt":0.3652423886650845,"score_spread":0.3317342439215861,"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."}}