{"id":"W4399732994","doi":"10.22148/001c.116368","title":"Exploring Gender Differences in Fatwa through Machine Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Cultural Analytics","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Popularity; Context (archaeology); Computer science; Preprocessor; Margin (machine learning); Artificial intelligence; Machine learning; Thematic analysis; Data science; Psychology; Social psychology; Qualitative research; Sociology; Social science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00294316,0.0005486188,0.0004546012,0.002912832,0.0006712321,0.001731393,0.0005608741,0.0006631403,0.004252931],"category_scores_gemma":[0.0162943,0.0001611283,0.0005926433,0.002180709,0.000398989,0.002227243,0.0009250794,0.0008787919,0.002650022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006045235,"about_ca_system_score_gemma":0.0004940107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004972354,"about_ca_topic_score_gemma":0.009101549,"domain_scores_codex":[0.9984961,0.0005332301,0.0001340285,0.0003975513,0.0002540605,0.0001850809],"domain_scores_gemma":[0.9865471,0.009588533,0.001328141,0.0008413059,0.001282984,0.000411944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001027072,0.0004718894,0.8156814,0.0007090408,0.0001685117,0.0003464485,0.005629693,0.002009499,0.009153786,0.00318562,0.01637061,0.1452463],"study_design_scores_gemma":[0.00006002291,0.0004944847,0.8423656,0.000341706,0.0001729818,0.001080975,0.01399511,0.065797,0.007772036,0.01062802,0.05719349,0.00009869423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423413,0.002390475,0.02068863,0.001140043,0.0001892148,0.0002336932,0.02280416,0.0005974342,0.009615019],"genre_scores_gemma":[0.9602129,0.0005130479,0.01328368,0.0002257381,0.0001323175,0.0002368675,0.02116809,0.00008887548,0.004138492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004972354,"threshold_uncertainty_score":0.0155651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2739161805313298,"score_gpt":0.3164628369341733,"score_spread":0.04254665640284344,"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."}}