{"id":"W2889107391","doi":"10.25336/csp29408","title":"Demographic Dividends: Emerging Challenges and Policy Implications","year":2018,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Ontario","funders":"","keywords":"Pace; Dividend; Economics; Sociology; Regional science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.003601842,0.0007813851,0.001115064,0.002139384,0.003760684,0.01090959,0.001518073,0.003266558,0.03801101],"category_scores_gemma":[0.007403231,0.0003768649,0.0004000489,0.007311944,0.007790457,0.007769471,0.00369776,0.006503185,0.003004393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01881333,"about_ca_system_score_gemma":0.04047066,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4208545,"about_ca_topic_score_gemma":0.5283753,"domain_scores_codex":[0.9989912,0.0002117354,0.00003038266,0.0001479323,0.0003098641,0.0003089135],"domain_scores_gemma":[0.9955425,0.001909625,0.000265858,0.0001140031,0.001249909,0.0009181092],"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":[0.00003907553,0.00003021831,0.001808835,0.0004928844,0.00001589531,0.00009645855,0.002882943,0.0006067269,0.0001166241,0.2718185,0.5251689,0.1969228],"study_design_scores_gemma":[0.0000121622,0.00001442143,0.0067632,0.00260909,0.00001171443,0.00005793338,0.0120551,0.0002965837,0.00005633891,0.07513303,0.9029452,0.00004532129],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004127057,0.3577298,0.0009604296,0.5404866,0.007112669,0.00002808056,0.001199982,0.0000650104,0.08829049],"genre_scores_gemma":[0.1319752,0.7647269,0.002211372,0.02821718,0.008234397,0.00007754156,0.00118106,0.0001323839,0.06324396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5791456,"threshold_uncertainty_score":0.8368096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0799659124754573,"score_gpt":0.3905585307232838,"score_spread":0.3105926182478265,"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."}}