{"id":"W7125911113","doi":"10.46609/ijsser.2025.v10i11.027","title":"Gendered Incidence of Indirect Taxation in India’s Informal Economy: Evidence from Uttar Pradesh","year":2025,"lang":"","type":"article","venue":"International Journal of Social Science and Economic Research","topic":"Gender, Labor, and Family Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Incidence (geometry); Uttar pradesh; Unemployment; Per capita; Tax incidence; Quarter (Canadian coin)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.01537042,0.0001789248,0.0004936795,0.002039448,0.0006928748,0.0007657511,0.002484882,0.0002556237,0.0001670888],"category_scores_gemma":[0.003070466,0.0001914401,0.0001291461,0.00128127,0.003380342,0.004296144,0.0006044612,0.0008402289,0.00001495472],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002714898,"about_ca_system_score_gemma":0.01204006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00629453,"about_ca_topic_score_gemma":0.001703599,"domain_scores_codex":[0.9953888,0.0004254807,0.001474548,0.0004358879,0.001589796,0.0006854992],"domain_scores_gemma":[0.9947131,0.001050848,0.001470666,0.000146294,0.002401736,0.0002173529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000387395,0.0001719068,0.7814388,0.00004601431,0.0002241507,0.00002565617,0.1338503,0.000241049,0.001257598,0.05446059,0.0006289458,0.02726757],"study_design_scores_gemma":[0.0008005166,0.0001333047,0.9227029,0.0003871035,0.00001716164,0.000003605054,0.0507043,0.00260342,0.0004072766,0.02060693,0.001445807,0.0001876634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622965,0.0006996905,0.0000661094,0.002502528,0.001869104,0.0002728033,0.00003408491,0.000003193677,0.03225593],"genre_scores_gemma":[0.9940737,0.004782728,0.0001408486,0.000163212,0.0006179471,0.000006376466,0.00000204091,0.00000612956,0.0002069896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1412641,"threshold_uncertainty_score":0.9993319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07395774939260744,"score_gpt":0.4076444930242178,"score_spread":0.3336867436316104,"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."}}