{"id":"W3209754500","doi":"10.18278/jep.1.3.5","title":"Older Adults and the Digital Divide in Romania: Implications for the COVID‐19 Pandemic","year":2021,"lang":"en","type":"article","venue":"Journal of Elder Policy","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Decree; Pandemic; The Internet; Population; Romanian; Government (linguistics); Political science; Digital divide; Human rights; Convention; Economic growth; Public relations; Sociology; Coronavirus disease 2019 (COVID-19); Law; Medicine; Information and Communications Technology; Demography; Economics; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002036549,0.0002105317,0.0004200938,0.001515419,0.002280235,0.003210985,0.0006049722,0.0008311167,0.005879758],"category_scores_gemma":[0.006741115,0.0001968262,0.0004461134,0.001998818,0.002687137,0.003562883,0.004782584,0.002006308,0.0002315184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002348822,"about_ca_system_score_gemma":0.002409907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01586543,"about_ca_topic_score_gemma":0.0212158,"domain_scores_codex":[0.9986354,0.000542057,0.00008288029,0.0001236795,0.0001461449,0.0004698886],"domain_scores_gemma":[0.9973701,0.0007008135,0.0008455924,0.0001100156,0.0003141884,0.0006592524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002183374,0.0003518163,0.6852937,0.001066028,0.0001155913,0.002664811,0.14478,0.0002994407,0.0003289287,0.0534194,0.01490622,0.09655573],"study_design_scores_gemma":[0.00001489367,0.00007612257,0.7628095,0.002098038,0.00004162197,0.0007403389,0.2039572,0.0003257278,0.00008785409,0.005483296,0.02432767,0.00003779242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9340131,0.01444858,0.0002493387,0.03095698,0.0002322288,0.00003694993,0.0005606106,0.000005925405,0.01949627],"genre_scores_gemma":[0.9954781,0.002986327,0.00008428103,0.0009868577,0.00007884354,0.00001496272,0.00007369464,0.000002447315,0.0002945593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01586543,"threshold_uncertainty_score":0.03154612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030241247560933,"score_gpt":0.3495979636401986,"score_spread":0.3192955511645893,"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."}}