{"id":"W3045326779","doi":"10.5430/ijba.v11n4p82","title":"Mediating Role of Employee Decision on Relationship Between Employee Separation Planning and Retirement Preparedness in Kenya","year":2020,"lang":"en","type":"article","venue":"International Journal of Business Administration","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preparedness; Sample (material); Government (linguistics); Separation (statistics); Null hypothesis; Business; Retirement planning; Logistic regression; Population; Stratified sampling; Marketing; Actuarial science; Economics; Management; Environmental health; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0009310745,0.0001103481,0.0002162269,0.0001696644,0.0001042778,0.0001046821,0.0002481647,0.0000886922,0.00001953456],"category_scores_gemma":[0.00240849,0.0001090131,0.00005186705,0.0002735687,0.0000963623,0.0005419139,0.00003429908,0.0001454563,0.000001821983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002194837,"about_ca_system_score_gemma":0.000329365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009648572,"about_ca_topic_score_gemma":0.0001694211,"domain_scores_codex":[0.9975405,0.0001615674,0.000882505,0.0001803337,0.001108776,0.0001263072],"domain_scores_gemma":[0.9980276,0.0005323778,0.0006311988,0.00007135521,0.0006263399,0.0001111286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003156613,0.00007082137,0.9804471,0.0000182284,0.00002137653,0.000006711397,0.01284054,0.0009094019,0.0002860331,0.001601827,0.00004064425,0.003441622],"study_design_scores_gemma":[0.0006391326,0.0002767011,0.9888189,0.0002806914,0.0000217706,0.000001667667,0.004056318,0.0001219745,0.0006902547,0.004808845,0.0001776245,0.0001061556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948366,0.00006060205,0.001435634,0.002259384,0.0003314044,0.0001959371,0.000009824912,0.00001117485,0.0008593953],"genre_scores_gemma":[0.9987724,0.00002989179,0.0005139655,0.00006102367,0.0005794316,0.00000516402,0.00002417404,0.000008775519,0.000005127423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008784217,"threshold_uncertainty_score":0.4445425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2380734645960663,"score_gpt":0.4643245997410338,"score_spread":0.2262511351449675,"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."}}