{"id":"W4402669780","doi":"10.18653/v1/2024.gebnlp-1.24","title":"Analysis of Annotator Demographics in Sexism Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Demographics; Computer science; Natural language processing; Sociology; Demography","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":[0.0257212,0.001204546,0.0008306499,0.001905379,0.00191303,0.001834165,0.0009298455,0.00105444,0.001757649],"category_scores_gemma":[0.06948054,0.0004233747,0.0005865456,0.001369001,0.0008141653,0.003201631,0.00226959,0.001418871,0.002276062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007945211,"about_ca_system_score_gemma":0.00109087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004735706,"about_ca_topic_score_gemma":0.01603013,"domain_scores_codex":[0.9833676,0.01031192,0.001013577,0.002588095,0.002196876,0.0005220949],"domain_scores_gemma":[0.9317903,0.04581852,0.004713808,0.005637473,0.01093651,0.001103405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002906312,0.0005909656,0.6395561,0.001290069,0.000640379,0.0009341535,0.01071258,0.007586142,0.04207009,0.001822936,0.03536271,0.2565277],"study_design_scores_gemma":[0.0002832336,0.001275804,0.5490295,0.00087078,0.0009089942,0.002773627,0.01487256,0.2503699,0.06648809,0.008198861,0.1045227,0.0004058875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8984495,0.003125734,0.07390855,0.001543816,0.001084347,0.0005530476,0.00763158,0.002059736,0.01164371],"genre_scores_gemma":[0.949403,0.0005017383,0.02970165,0.0007347623,0.0002860465,0.0006195019,0.01284569,0.0004898027,0.005417798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0257212,"threshold_uncertainty_score":0.1360283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007130768271533387,"score_gpt":0.2379038436104169,"score_spread":0.2307730753388835,"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."}}