{"id":"W4285325322","doi":"10.55671/0160-4341.1155","title":"Knowing Our History: How the Structural Context of California’s Aging Network Evolved","year":2021,"lang":"en","type":"article","venue":"Humboldt Journal of Social Relations","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Aging","funders":"","keywords":"Governor; Context (archaeology); Dignity; State (computer science); Influencer marketing; Agency (philosophy); Gerontology; Political science; Public relations; Psychology; Sociology; Law; History; Medicine; Business; Engineering; Social science; Marketing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001229794,0.00008967538,0.0002517537,0.00003293583,0.001146139,0.00006497813,0.0002210692,0.00008846325,0.0003328272],"category_scores_gemma":[0.00065123,0.00007280552,0.0002995706,0.0002236632,0.0002500697,0.000228644,0.00003835637,0.0003044695,0.000006153648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006649873,"about_ca_system_score_gemma":0.0008814461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002608467,"about_ca_topic_score_gemma":0.001319257,"domain_scores_codex":[0.9980962,0.0005128278,0.0004170093,0.00009922748,0.0006146025,0.0002601659],"domain_scores_gemma":[0.9985369,0.0001919425,0.0005201524,0.0001011952,0.0005566775,0.00009310206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004668411,0.0001635469,0.3213438,0.00005155097,0.0006111583,0.00002026016,0.2181212,0.0007042835,0.0006090854,0.2247416,0.224379,0.00920791],"study_design_scores_gemma":[0.001137241,0.00007127405,0.1290604,0.0001796795,0.0004578571,0.000007773225,0.2723695,0.0001247224,0.00006210506,0.02827981,0.567893,0.0003565918],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8403507,0.007785695,0.0007732611,0.09964842,0.005821962,0.0004951723,0.00002844655,0.00004340059,0.04505287],"genre_scores_gemma":[0.9954872,0.0000736131,0.0001944515,0.0001576264,0.001520826,0.000002000214,0.000003004294,0.000009372622,0.002551896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.343514,"threshold_uncertainty_score":0.8815293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1980931903257759,"score_gpt":0.3879767490672393,"score_spread":0.1898835587414635,"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."}}