{"id":"W2969398105","doi":"10.1017/dem.2019.4","title":"Forecasting human capital of EU member countries accounting for sociocultural determinants","year":2019,"lang":"en","type":"article","venue":"Journal of Demographic Economics","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Microsimulation; Human capital; Sociocultural evolution; Educational attainment; European union; Demographic economics; Projection (relational algebra); Projections of population growth; Population; Literacy; Economics; Econometrics; Geography; Economic growth; Political science; Sociology; Population growth; Computer science; Demography; International economics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004214663,0.000171932,0.0006459893,0.0006091307,0.0001841692,0.0002164888,0.0005790818,0.000135929,0.000064411],"category_scores_gemma":[0.0004303546,0.0001319497,0.0006217604,0.0002679786,0.0001245616,0.0009591349,0.00005178786,0.0001806169,0.000006704915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002846179,"about_ca_system_score_gemma":0.00008717037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001157375,"about_ca_topic_score_gemma":0.0001047336,"domain_scores_codex":[0.9970876,0.00005342762,0.00191912,0.0002491811,0.0004046991,0.00028596],"domain_scores_gemma":[0.9948072,0.0009773867,0.002834451,0.0002474023,0.00104714,0.00008642443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001446044,0.00006362651,0.9790452,0.00007200891,0.0001762396,0.000002606838,0.002033297,0.005376456,0.000627926,0.004866254,0.0002886151,0.007303193],"study_design_scores_gemma":[0.01148569,0.002946067,0.2137957,0.0009108381,0.0007574176,0.0008059157,0.0428828,0.3276721,0.003053027,0.3836552,0.01002874,0.002006572],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979197,0.000184197,0.000440921,0.0001662071,0.0006961757,0.0001763271,0.0000245635,0.00000655676,0.0003853837],"genre_scores_gemma":[0.9972675,0.00009786461,0.002237106,0.00009733608,0.0001709989,0.000002424197,0.00000236232,0.00001863819,0.0001057243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7652495,"threshold_uncertainty_score":0.5380751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1047314764683549,"score_gpt":0.3451837757507438,"score_spread":0.240452299282389,"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."}}