{"id":"W4321074103","doi":"10.2139/ssrn.4358818","title":"Consumption Smoothing in Russia","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Russia and Soviet political economy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Consumption (sociology); Smoothing; Economics; Econometrics; Political science; Statistics; Mathematics; Sociology; Social science","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":[0.0006188225,0.00008074319,0.000446243,0.0005922486,0.0006733952,0.001711798,0.0002182331,0.0005269965,0.00338227],"category_scores_gemma":[0.002287581,0.0001217371,0.0002899565,0.001083745,0.000494417,0.0005329045,0.0009752041,0.0006372581,0.0003609651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521482,"about_ca_system_score_gemma":0.001041625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0177329,"about_ca_topic_score_gemma":0.01368665,"domain_scores_codex":[0.9997023,0.00009594513,0.00001284583,0.00004782572,0.00003510573,0.0001058916],"domain_scores_gemma":[0.9990758,0.0003864849,0.0002388446,0.0001016264,0.00008184789,0.0001154663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001825825,0.000326271,0.4320932,0.0001894293,0.0003188516,0.000850009,0.006260142,0.01757381,0.002623698,0.4629512,0.009654878,0.06533264],"study_design_scores_gemma":[0.0001075031,0.0002544313,0.7778242,0.0001047155,0.0002562694,0.0002748297,0.007361681,0.02045073,0.001465734,0.1621782,0.02967908,0.00004260116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789418,0.0006359503,0.000517536,0.00188742,0.00001740184,0.000003060732,0.0002803181,0.00002087056,0.01769564],"genre_scores_gemma":[0.997358,0.0001445095,0.00004323482,0.00003073586,0.00001186467,0.000001447781,0.00006456041,0.000003341512,0.002342199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0177329,"threshold_uncertainty_score":0.03525937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0236635985739354,"score_gpt":0.3226664566492116,"score_spread":0.2990028580752762,"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."}}