{"id":"W4312103813","doi":"10.1093/geroni/igac059.858","title":"ACHIEVING DIGITAL EQUITY FOR OLDER PERSONS WITH EMERGING TECHNOLOGY: THE CASE OF NORTH AMERICA","year":2022,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital divide; Residence; Equity (law); Internet access; The Internet; Ethnic group; Economic growth; Geography; Business; Demographic economics; Political science; Economics; Computer science; World Wide Web","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":[0.004357354,0.0002382914,0.0003197677,0.001034502,0.009432421,0.00509688,0.0009241328,0.003520366,0.002903963],"category_scores_gemma":[0.00509287,0.0001622281,0.000560383,0.001280871,0.005071616,0.005281867,0.007268975,0.0038133,0.0001410479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00863674,"about_ca_system_score_gemma":0.008835192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1431226,"about_ca_topic_score_gemma":0.2617077,"domain_scores_codex":[0.9975716,0.0009819758,0.00007326646,0.0001270437,0.00026351,0.0009825904],"domain_scores_gemma":[0.9967175,0.001258935,0.0003338545,0.0001491933,0.0005677075,0.0009727059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00007730356,0.0005116239,0.0903277,0.000381473,0.00005458377,0.01154289,0.4529057,0.0004849268,0.0006076518,0.3294937,0.03580047,0.07781198],"study_design_scores_gemma":[0.00003615263,0.0001274472,0.06584469,0.001683486,0.00006360403,0.002642655,0.6960103,0.00105606,0.0004200373,0.04198746,0.1900606,0.00006750157],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7577574,0.00683076,0.0009200124,0.1429707,0.0003929369,0.00007057001,0.00006319234,0.00001043723,0.09098405],"genre_scores_gemma":[0.9851859,0.002896108,0.0004100234,0.006983982,0.0001795904,0.00005708248,0.00002095206,0.000004595531,0.004261627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1431226,"threshold_uncertainty_score":0.2845791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156967719385359,"score_gpt":0.3193315086078525,"score_spread":0.2977618314139989,"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."}}