{"id":"W2900461945","doi":"10.1002/app5.267","title":"Understanding the spatial disparities and vulnerability of population aging in China","year":2018,"lang":"en","type":"article","venue":"Asia & the Pacific Policy Studies","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Fundamental Research Funds for the Central Universities; Beijing Normal University; National Natural Science Foundation of China","keywords":"Census; China; Geography; Population; Population ageing; Vulnerability (computing); Socioeconomics; East Asia; Rural area; Demography; Economics; Political science; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009034744,0.0001771057,0.0002230779,0.001821991,0.0005293828,0.0004812644,0.0002518357,0.000144896,0.001171117],"category_scores_gemma":[0.002269581,0.00008832111,0.0003273848,0.002399539,0.0004227855,0.000776465,0.001039031,0.000206224,0.00004347038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109874,"about_ca_system_score_gemma":0.001822067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08083063,"about_ca_topic_score_gemma":0.0932368,"domain_scores_codex":[0.9996923,0.00007926102,0.00002899703,0.00006428727,0.00005155425,0.0000835784],"domain_scores_gemma":[0.9992617,0.0001554031,0.0002588994,0.00008323329,0.0001409709,0.00009993739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001158747,0.00001606878,0.9869776,0.00002726241,0.00005466286,0.00005174715,0.0008548485,0.000755324,0.00009784164,0.001517378,0.0002344609,0.009401083],"study_design_scores_gemma":[0.0000013417,0.00001298452,0.9945529,0.00002427115,0.00002989186,0.00002266395,0.001584094,0.001996411,0.0000413817,0.001225033,0.0005048494,0.000004284358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979195,0.0002947506,0.0002641459,0.0002500523,0.000004899201,0.000006502604,0.0002173445,0.000002952561,0.001039978],"genre_scores_gemma":[0.9996699,0.00009489763,0.00008983759,0.00001334268,0.000003182274,0.000003215272,0.00007512471,3.470236e-7,0.00005012681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08083063,"threshold_uncertainty_score":0.1607203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08366943881225183,"score_gpt":0.3631988219072912,"score_spread":0.2795293830950394,"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."}}