{"id":"W7130556539","doi":"10.11575/prism/51127","title":"Navigating the Digital Chasm: Digital Inequity and the Determinants among Racialized Seniors in Calgary, Alberta.","year":2025,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Digital divide; Ethnic group; General partnership; Immigration; Health equity; Population; Socioeconomic status; Equity (law); Inequality","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0008035356,0.0003335041,0.0003084145,0.001542994,0.005192577,0.002139121,0.001371288,0.0006387162,0.001694163],"category_scores_gemma":[0.001511611,0.0003141905,0.0002442216,0.002668458,0.001811875,0.0007498565,0.002458956,0.001111066,0.000221887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01098916,"about_ca_system_score_gemma":0.01493261,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.96014,"about_ca_topic_score_gemma":0.9842405,"domain_scores_codex":[0.9993432,0.00006761758,0.00002541431,0.00006698473,0.0001965986,0.0003002148],"domain_scores_gemma":[0.9991555,0.00007467031,0.0001589387,0.00002549582,0.0002087678,0.0003765741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008195531,0.0002269665,0.9194366,0.00009392944,0.00003113754,0.0006374757,0.05142058,0.00006268099,0.0003612665,0.0003278409,0.001758079,0.02556138],"study_design_scores_gemma":[0.000006330064,0.00007595791,0.8683822,0.00008433013,0.00002113947,0.0001120666,0.1293439,0.0001009884,0.00005600064,0.0001187193,0.001686668,0.00001188588],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977658,0.0003128152,0.0000304427,0.0004553485,0.00001025995,0.00002530872,0.0001250321,0.000002474109,0.001272593],"genre_scores_gemma":[0.9976931,0.0004269143,0.0001207661,0.0003841082,0.000007619633,0.00001786831,0.0001150349,0.000002205263,0.001232278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03985995,"threshold_uncertainty_score":0.08018935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564123322689993,"score_gpt":0.318431470225777,"score_spread":0.3027902369988771,"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."}}