{"id":"W7164892076","doi":"10.1080/17153379.2014.12557035","title":"Demographic Change and Inequality in Japan. Japanese Society Series.","year":2014,"lang":"en","type":"article","venue":"Pacific Affairs","topic":"Japanese History and Culture","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Demographic change; Inequality; Technological change; Demographic analysis; Demographic transition; Social inequality","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006991582,0.0005105892,0.0004029713,0.001886645,0.0008941934,0.0007769801,0.0005271635,0.0004239189,0.007514965],"category_scores_gemma":[0.001565837,0.0002332395,0.0002584457,0.005476451,0.0004891765,0.000998674,0.0009705529,0.0006118107,0.0007383976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001888307,"about_ca_system_score_gemma":0.005369873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1827378,"about_ca_topic_score_gemma":0.2600027,"domain_scores_codex":[0.9998764,0.00002021724,0.00002106338,0.00001940764,0.00003022803,0.0000325981],"domain_scores_gemma":[0.9987213,0.0001591438,0.0001756371,0.00005485415,0.0004533377,0.0004356634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001841727,0.00008191841,0.07802668,0.001314577,0.00009096912,0.0003134941,0.003746925,0.0004092934,0.0003683791,0.00367815,0.7629028,0.1488826],"study_design_scores_gemma":[0.00001502962,0.00005782249,0.7063498,0.0004677013,0.0001974724,0.0001776812,0.004870233,0.0003501498,0.0002314153,0.000684776,0.2865778,0.00002000179],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1335149,0.6616226,0.001120041,0.05968944,0.02701893,0.0001185113,0.05570104,0.0001684142,0.06104617],"genre_scores_gemma":[0.4826553,0.3802776,0.001520081,0.001724917,0.01262734,0.0003839864,0.03236426,0.00008206115,0.08836464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1827378,"threshold_uncertainty_score":0.3633482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02835145716867139,"score_gpt":0.2524386394654209,"score_spread":0.2240871822967496,"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."}}