{"id":"W4414359753","doi":"10.24963/ijcai.2025/1083","title":"What is Beneath Misogyny: Misogynous Memes Classification and Explanation","year":2025,"lang":"en","type":"article","venue":"","topic":"Gender, Feminism, and Media","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Modalities; Cyberspace; Classifier (UML); Context (archaeology); Multimodality; Ideology","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":[0.0007593336,0.001356025,0.0003976975,0.003313056,0.001167396,0.001579436,0.001238094,0.001554451,0.003393928],"category_scores_gemma":[0.004790315,0.0001979859,0.0007848189,0.001633213,0.0009294235,0.002812043,0.001975014,0.001133849,0.001870355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176386,"about_ca_system_score_gemma":0.0009151982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009402432,"about_ca_topic_score_gemma":0.02360906,"domain_scores_codex":[0.9991865,0.0002195711,0.00006116775,0.0002753676,0.0001505471,0.0001068207],"domain_scores_gemma":[0.9982907,0.0007567731,0.0002052945,0.0004264221,0.0002069116,0.0001138942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001062135,0.0005128108,0.1557622,0.002600575,0.0002896042,0.002404209,0.0113772,0.008532865,0.02302021,0.01649513,0.2445414,0.5334018],"study_design_scores_gemma":[0.0001133389,0.0002977166,0.1770582,0.001018979,0.0002581333,0.004491223,0.02443056,0.2243151,0.036545,0.04488429,0.486299,0.000288421],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6481429,0.008246488,0.15867,0.008327117,0.001631431,0.001117189,0.1132578,0.01911848,0.04148854],"genre_scores_gemma":[0.7658963,0.001416836,0.102281,0.001263491,0.0004063218,0.0006642038,0.1134205,0.0006582065,0.01399319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009402432,"threshold_uncertainty_score":0.01869541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04324325205234915,"score_gpt":0.3451613636605488,"score_spread":0.3019181116081997,"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."}}