{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004687067,0.0003408029,0.0006364014,0.001206162,0.001280918,0.002217017,0.001124849,0.001457039,0.01059057],"category_scores_gemma":[0.03585402,0.0002441541,0.0008692113,0.000909348,0.0006724948,0.001746558,0.002405774,0.0024571,0.001640343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001367377,"about_ca_system_score_gemma":0.002073641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007055021,"about_ca_topic_score_gemma":0.01002066,"domain_scores_codex":[0.9966587,0.001097242,0.0001589869,0.0002248473,0.0004737099,0.001386584],"domain_scores_gemma":[0.9650777,0.01383098,0.01384629,0.001049908,0.001563461,0.004631641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006604998,0.004931554,0.8591259,0.0001515123,0.0002375647,0.0004438582,0.001280847,0.005351051,0.001056558,0.005425093,0.00213984,0.1132512],"study_design_scores_gemma":[0.0001312681,0.00316266,0.9751649,0.0001436722,0.000281034,0.0002608273,0.003261245,0.007088692,0.002557328,0.005115642,0.00277925,0.00005341709],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946511,0.0002500371,0.0004187114,0.0007658267,0.00002513682,0.00003446061,0.0003906483,0.00002659483,0.003437499],"genre_scores_gemma":[0.9958035,0.00008515323,0.0001372875,0.00007574834,0.00001581611,0.000010643,0.0002902933,0.000004828457,0.003576742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01059057,"threshold_uncertainty_score":0.03542894,"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."}}