{"id":"W4321123802","doi":"10.2139/ssrn.4357558","title":"Marital Sorting and Inequality: How Educational Categorization Matters","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Intergenerational and Educational Inequality Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Categorization; Inequality; Sorting; Psychology; Demographic economics; Political science; Sociology; Social psychology; Computer science; Economics; Artificial intelligence; Mathematics; Algorithm","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":[0.003023942,0.0002651949,0.0006108059,0.001587826,0.002453641,0.004654609,0.001245567,0.001559611,0.02093193],"category_scores_gemma":[0.01585497,0.0002458024,0.0004294913,0.002981975,0.003832939,0.005528769,0.002990144,0.001306813,0.0008212704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001191877,"about_ca_system_score_gemma":0.001350993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0235687,"about_ca_topic_score_gemma":0.03056178,"domain_scores_codex":[0.9977041,0.001321031,0.00005127264,0.0002402111,0.0002659658,0.0004174441],"domain_scores_gemma":[0.9871097,0.00910426,0.001403685,0.0006206663,0.0006615199,0.00110015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005374849,0.0010187,0.6820308,0.0001842523,0.0003787685,0.0005109258,0.04034968,0.0006360951,0.0003614496,0.1809403,0.007645524,0.08540604],"study_design_scores_gemma":[0.0000666983,0.0001546965,0.7776331,0.0003718178,0.0002807005,0.0001847002,0.07234314,0.002013639,0.0002537052,0.1360291,0.01060905,0.0000596663],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9409653,0.004261445,0.001376524,0.01739317,0.0001231096,0.00002068505,0.0003780343,0.00001497175,0.03546661],"genre_scores_gemma":[0.997918,0.000352802,0.0001036943,0.000225028,0.00004203164,0.000007722419,0.00005489985,0.000006143219,0.00128981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0235687,"threshold_uncertainty_score":0.07002425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03323878030008539,"score_gpt":0.3309884359889572,"score_spread":0.2977496556888719,"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."}}