{"id":"W7080239443","doi":"","title":"The digital divide and population aging in Chile: diagnosis, public policies, and intergenerational impact","year":2025,"lang":"en","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital divide; Digital inclusion; Population ageing; Context (archaeology); Socioeconomic status; Public policy; Population; Fertility; Inclusion (mineral)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001135392,0.0001800041,0.0001922418,0.00380925,0.001794857,0.003219243,0.0004659449,0.0005672503,0.004304731],"category_scores_gemma":[0.003724114,0.0001018644,0.0002178281,0.003879468,0.002757259,0.002753634,0.00358195,0.0007856955,0.000127346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005924694,"about_ca_system_score_gemma":0.004845709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05101767,"about_ca_topic_score_gemma":0.05856696,"domain_scores_codex":[0.9993601,0.000220177,0.0000362199,0.00005338415,0.0001129284,0.000217164],"domain_scores_gemma":[0.9982702,0.0005343652,0.0005399973,0.0001025786,0.0002848459,0.0002679735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001003517,0.0004065095,0.5581972,0.0008168575,0.00005996455,0.001589893,0.0436306,0.0008951601,0.0004049926,0.1720155,0.008067344,0.2138157],"study_design_scores_gemma":[0.00001581418,0.0000959869,0.7006145,0.001854725,0.00006066574,0.0008048321,0.1725136,0.001062253,0.0004573716,0.02915956,0.09331848,0.00004224413],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8239354,0.02115712,0.0008007478,0.03700628,0.0001359242,0.00009047053,0.001036049,0.00002997947,0.115808],"genre_scores_gemma":[0.9893454,0.007137953,0.0001899415,0.0003358073,0.00006638033,0.00003648584,0.0001802267,0.000003032788,0.002704724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05101767,"threshold_uncertainty_score":0.1014414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007569341851014683,"score_gpt":0.240981862524817,"score_spread":0.2334125206738023,"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."}}