{"id":"W2106208348","doi":"10.1017/s0144686x14000713","title":"Previous employment histories and quality of life in older ages: sequence analyses using SHARELIFE","year":2014,"lang":"en","type":"article","venue":"Ageing and Society","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fifth Framework Programme; AGE-WELL","keywords":"Unemployment; Demography; Gerontology; Quality of life (healthcare); Psychology; Demographic economics; Medicine; Sociology; Economics; Economic growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001701599,0.0001028014,0.0002825132,0.00001685372,0.0003303145,0.00005249205,0.00009057952,0.00007973667,0.00001656897],"category_scores_gemma":[0.0004410006,0.00009535367,0.00008628455,0.0001405241,0.0005139673,0.0001377323,0.00005960295,0.00008149392,2.44397e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001503413,"about_ca_system_score_gemma":0.00007147978,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03484184,"about_ca_topic_score_gemma":0.002011539,"domain_scores_codex":[0.9985543,0.0003090513,0.0003413658,0.0002603506,0.0003124787,0.0002224192],"domain_scores_gemma":[0.9993658,0.0001855405,0.0001368277,0.000145402,0.0000548005,0.0001116342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006855712,0.0001114663,0.796064,0.0003435804,0.00004882596,5.393687e-7,0.1973211,0.00006111928,0.001293447,0.002191582,0.0001993155,0.002358108],"study_design_scores_gemma":[0.001279335,0.0001259958,0.9326752,0.0003853682,0.0001064945,2.783451e-7,0.05702721,0.001635432,0.0001690816,0.002012663,0.003944055,0.0006388819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976695,0.0009305943,0.0001690196,0.0003231643,0.00007522349,0.0001649202,0.000003539034,0.00002515321,0.0006388611],"genre_scores_gemma":[0.9983662,0.0007706544,0.0003948441,0.0002815193,0.00006274413,0.000007124951,0.000002236796,0.00000581049,0.0001088571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1402939,"threshold_uncertainty_score":0.9715852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4133665777161307,"score_gpt":0.4818673642352692,"score_spread":0.06850078651913855,"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."}}