{"id":"W4361990584","doi":"10.57106/scientia.v12i1.146","title":"Second Wind: Understanding How Academics from the Philippines Adjust to Retirement","year":2023,"lang":"en","type":"article","venue":"Scientia - The International Journal on the Liberal Arts","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Life expectancy; Promotion (chess); Gerontology; Psychology; Meaning (existential); Face (sociological concept); Sociology; Retirement age; Social psychology; Medicine; Political science; Social science; Population; Demography; Politics; Pension","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003877816,0.0001511517,0.0001137923,0.00009156519,0.002359016,0.001409177,0.002206136,0.00005323743,0.001681014],"category_scores_gemma":[0.0004939582,0.00007345427,0.0001750193,0.0004449269,0.0004607499,0.0003069476,0.0003379085,0.0004525159,0.0002902657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006098346,"about_ca_system_score_gemma":0.0001252904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001290244,"about_ca_topic_score_gemma":0.001266673,"domain_scores_codex":[0.9965063,0.0003459869,0.0002892916,0.0002730588,0.002142549,0.000442854],"domain_scores_gemma":[0.9984889,0.0007001354,0.0001969503,0.0002843138,0.0001598489,0.000169877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001027127,0.00008311852,0.005638692,0.000002316973,0.0002829883,0.00001410875,0.03880904,0.0006358753,0.001241707,0.1320464,0.8192252,0.001917834],"study_design_scores_gemma":[0.0004466248,0.00009394172,0.01672446,0.0002018064,0.00004711307,0.000006591687,0.02888369,0.0003264587,0.0007123721,0.3064739,0.6457808,0.000302245],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.497374,0.00002969853,0.00005397368,0.4899019,0.005065373,0.0002997275,0.00003080228,0.00003971441,0.007204832],"genre_scores_gemma":[0.9727347,0.0001364384,0.00001882195,0.01260324,0.00277255,0.00001273242,0.000007586123,0.00001437303,0.01169949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4772986,"threshold_uncertainty_score":0.9996275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4109333789682121,"score_gpt":0.4172327239426272,"score_spread":0.006299344974415089,"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."}}