{"id":"W1978457527","doi":"10.1006/jvbe.1999.1736","title":"Work and Nonwork Predictors of Employees' Retirement Ages","year":2000,"lang":"en","type":"article","venue":"Journal of Vocational Behavior","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":247,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Central Michigan University","keywords":"Psychology; Government (linguistics); Variance (accounting); Work (physics); Set (abstract data type); Retirement age; Demographic economics; Social psychology; Gerontology; Applied psychology; Business; Pension; Finance; Economics; Medicine; Accounting; Engineering","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":[0.001459035,0.0003020586,0.0002487361,0.0009963794,0.0006702294,0.0008741324,0.0004758409,0.0008299525,0.005398117],"category_scores_gemma":[0.007238429,0.0003376735,0.0005791738,0.0005093662,0.0003006631,0.0006157095,0.0006406765,0.0009635354,0.0007321153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156984,"about_ca_system_score_gemma":0.0005256495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01129225,"about_ca_topic_score_gemma":0.02116623,"domain_scores_codex":[0.9996125,0.0001341592,0.00003870143,0.00004495828,0.00004623075,0.000123397],"domain_scores_gemma":[0.9911527,0.003748657,0.001810248,0.0004232627,0.0006194722,0.002245635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001902645,0.0001119623,0.9981689,0.000003079668,0.00001647246,0.00001977729,0.000112229,0.00004864751,0.00006033335,0.00001796088,0.00008057679,0.00116983],"study_design_scores_gemma":[0.000003440973,0.00004171011,0.9993074,0.000003104435,0.00001273357,0.00001921304,0.00036255,0.0001503334,0.00001534321,0.00002378646,0.00005867748,0.000001632984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991641,0.0001291144,0.00002416499,0.0000956726,0.00001041004,0.000002171759,0.0001139016,0.000002095983,0.0004584675],"genre_scores_gemma":[0.9990206,0.00006734358,0.0000191894,0.00001256581,0.00002088138,0.000002600086,0.0001176673,0.000001530427,0.0007376311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01129225,"threshold_uncertainty_score":0.02245307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1454454727122876,"score_gpt":0.4167153272892176,"score_spread":0.27126985457693,"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."}}