{"id":"W4386783354","doi":"10.1016/j.eswa.2023.121542","title":"Nbias: A natural language processing framework for BIAS identification in text","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; Vector Institute","funders":"Vector Institute; Government of Ontario; Canadian Institute for Advanced Research","keywords":"Computer science; Security token; Transformer; Identification (biology); Artificial intelligence; Variety (cybernetics); Natural language processing; Data science; Machine learning; Data mining; Computer security","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":[0.007843419,0.001581865,0.001561707,0.004452778,0.001647088,0.003457221,0.002632975,0.001722047,0.009321353],"category_scores_gemma":[0.02289441,0.0009764062,0.00209186,0.002510108,0.00107627,0.005274282,0.003410903,0.003140517,0.006419578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271044,"about_ca_system_score_gemma":0.003058372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005253989,"about_ca_topic_score_gemma":0.007923733,"domain_scores_codex":[0.9952187,0.001957885,0.000460486,0.001000082,0.001137672,0.0002252405],"domain_scores_gemma":[0.9876757,0.007404427,0.0008877769,0.00133092,0.002304476,0.0003967218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001029109,0.0003651328,0.005741916,0.00180768,0.0005018627,0.0004819155,0.001975282,0.02281572,0.02806102,0.1401773,0.06128423,0.7357588],"study_design_scores_gemma":[0.0001387353,0.0001445634,0.00193795,0.0002445545,0.0002221908,0.0004069886,0.0003887447,0.7047128,0.01839439,0.2041938,0.06909856,0.0001167045],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001268287,0.0001871107,0.9894083,0.000179105,0.000063972,0.0001291822,0.001061777,0.007227214,0.0004751197],"genre_scores_gemma":[0.05254018,0.0003245142,0.9376739,0.000286996,0.0002876215,0.0006214611,0.004311907,0.00142858,0.002524876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009321353,"threshold_uncertainty_score":0.04148048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03851822480585686,"score_gpt":0.3238614681034604,"score_spread":0.2853432432976035,"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."}}