{"id":"W4390556203","doi":"10.1007/978-981-99-4902-1_5","title":"Mixture Models: Identifying Consumption Classes in Post-liberalization India","year":2023,"lang":"en","type":"book-chapter","venue":"Contributions to economics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Mixture model; Consumption (sociology); Class (philosophy); Econometrics; Probabilistic logic; Mathematics; Maximization; Expectation–maximization algorithm; Population; Statistics; Computer science; Mathematical optimization; Maximum likelihood; Artificial intelligence; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001051607,0.0002596253,0.000451589,0.0004463768,0.0005249019,0.0002624299,0.0003241304,0.0007397399,0.0002938867],"category_scores_gemma":[0.0004219745,0.0003295671,0.0001676885,0.00009825263,0.0001503188,0.0005682246,0.0001160167,0.0003735201,0.0009342711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001717769,"about_ca_system_score_gemma":0.0006488177,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009832542,"about_ca_topic_score_gemma":0.02269127,"domain_scores_codex":[0.9981733,0.0001020339,0.0006696046,0.0004574874,0.0001516128,0.0004459468],"domain_scores_gemma":[0.9985393,0.0002500538,0.0003063515,0.0003115199,0.0004147198,0.0001780788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001527654,0.00001428807,0.0002378418,0.00002104013,0.00004011718,0.00000307497,0.001987841,0.000558674,0.000002419653,0.9935484,0.002873509,0.000697531],"study_design_scores_gemma":[0.0007419257,0.00004245939,0.002779303,0.0002947083,0.0001039903,0.000001558949,0.0005390578,0.001279027,0.00001353983,0.6281097,0.3650826,0.001012123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02214292,0.001238477,0.01294571,0.009260911,0.008645507,0.004300876,0.01083924,0.0009374497,0.9296889],"genre_scores_gemma":[0.2818367,0.01968912,0.0003763993,0.004645244,0.002990497,0.0002598587,0.006577113,0.0002704761,0.6833546],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3654387,"threshold_uncertainty_score":0.9999157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06737331990431537,"score_gpt":0.3296153851811758,"score_spread":0.2622420652768604,"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."}}