{"id":"W4308595621","doi":"10.1287/mksc.2022.1404","title":"Cashing Out Retirement Savings at Job Separation","year":2022,"lang":"en","type":"article","venue":"Marketing Science","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Matching (statistics); Separation (statistics); Leakage (economics); Job loss; Business; Labour economics; Economics; Computer science; Unemployment; Mathematics; Statistics; Machine learning","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007245231,0.0001821349,0.0001820162,0.0004896952,0.00326327,0.0006255954,0.0007460573,0.00002094718,0.00224868],"category_scores_gemma":[0.001212733,0.0001913847,0.00009399476,0.001883343,0.0001730857,0.001497412,0.001711026,0.0001741809,0.0002679768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005310714,"about_ca_system_score_gemma":0.00006283399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004283501,"about_ca_topic_score_gemma":0.0002469065,"domain_scores_codex":[0.996839,0.0000574436,0.0003984866,0.0006705049,0.001501259,0.0005332312],"domain_scores_gemma":[0.9988747,0.00008645753,0.0003824809,0.0003978091,0.0002364912,0.00002205963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009032485,0.00004139073,0.9572504,0.00005973382,0.000003112577,0.00001945557,0.0003876631,0.0004937266,0.02414485,0.001853599,0.01398496,0.001670779],"study_design_scores_gemma":[0.0003959405,0.00002089964,0.7492872,0.00008136374,0.00007648739,0.000004574718,0.0004934828,0.05638379,0.0004051689,0.0003533196,0.1918196,0.0006781696],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620778,0.00003936194,0.00001923659,0.0004993974,0.0007616147,0.0002151251,0.00000156759,0.0001352716,0.0362506],"genre_scores_gemma":[0.9930266,0.000001752618,0.0003589726,0.001605343,0.0003668212,0.00003640934,0.00001813437,0.00001915703,0.00456687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2079632,"threshold_uncertainty_score":0.9986634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744664204652635,"score_gpt":0.2578890187439142,"score_spread":0.2404423766973878,"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."}}