{"id":"W3033231924","doi":"10.1111/joca.12304","title":"Gender differences in financial knowledge, attitudes, and behaviors: Accounting for socioeconomic disparities and psychological traits","year":2020,"lang":"en","type":"article","venue":"Journal of Consumer Affairs","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Financial literacy; Socioeconomic status; Psychology; Survey data collection; Population; Contrast (vision); Gender gap; Demography; Demographic economics; Social psychology; Finance; Economics; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003536569,0.0001787123,0.0004724759,0.0002112397,0.0001203214,0.0002426402,0.0001555541,0.00009376581,0.00008283104],"category_scores_gemma":[0.0002259879,0.0001517444,0.0001166678,0.0001354336,0.0001329708,0.0008689337,0.00008556296,0.0002016951,0.000008151321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001810419,"about_ca_system_score_gemma":0.00003307392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002145241,"about_ca_topic_score_gemma":0.0001249523,"domain_scores_codex":[0.9988332,0.0000152951,0.0005696865,0.0002312141,0.0001268662,0.0002237568],"domain_scores_gemma":[0.999256,0.0001080837,0.0004000115,0.00004767989,0.0001571479,0.00003104515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001044829,0.00009912124,0.9933532,0.0001734808,0.00001477603,0.000008013027,0.000243266,9.750148e-7,0.0001297922,0.001517677,0.001627233,0.002727934],"study_design_scores_gemma":[0.001142678,0.00004612815,0.9946703,0.00007429764,0.0001504809,0.000005154143,0.0002873962,0.0004018383,0.000004820805,0.0009015905,0.002116652,0.0001986203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996864,0.002060488,0.0000471523,0.0004770019,0.0001533421,0.0001508308,0.000004258976,0.00001196065,0.0002310125],"genre_scores_gemma":[0.9984334,0.0001661733,0.0003987597,0.0003894776,0.0005764331,0.000007103442,0.000002867404,0.00001353016,0.00001226699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002529314,"threshold_uncertainty_score":0.6187956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0475939070241856,"score_gpt":0.2854194164215044,"score_spread":0.2378255093973188,"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."}}