{"id":"W4294969104","doi":"10.3390/admsci12030112","title":"Digital Divide: Barriers to Accessing Online Government Services in Canada","year":2022,"lang":"en","type":"article","venue":"Administrative Sciences","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Digital divide; Equity (law); Government (linguistics); Business; Bivariate analysis; Logit; Public economics; Welfare; Rural area; Universal design; Economic growth; Economics; The Internet; Computer science; Political science; Econometrics; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000846548,0.0001892287,0.0004013431,0.00204044,0.007389095,0.003602549,0.001093573,0.000514238,0.009634693],"category_scores_gemma":[0.005850088,0.0002398533,0.0004282997,0.005084997,0.001706334,0.001316797,0.002514021,0.001257494,0.0003983029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0587123,"about_ca_system_score_gemma":0.10105,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.998312,"about_ca_topic_score_gemma":0.9988276,"domain_scores_codex":[0.9983153,0.00008262364,0.00005910265,0.0001277198,0.0005574796,0.0008577922],"domain_scores_gemma":[0.9950478,0.0006581713,0.0008049512,0.0001198111,0.001476737,0.001892549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001319936,0.0002267718,0.9081421,0.0001189372,0.00003877515,0.0006649193,0.01971727,0.0007883355,0.0003047613,0.01192504,0.009751513,0.04818939],"study_design_scores_gemma":[0.00001976106,0.00003364813,0.9259536,0.0002276467,0.00002674468,0.0001701039,0.05441235,0.002221398,0.0002025159,0.0008381155,0.01584555,0.00004854451],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770966,0.0006237642,0.000121487,0.003511566,0.00001704367,0.00003739046,0.001393581,0.00001441682,0.01718418],"genre_scores_gemma":[0.9948821,0.0004868199,0.0001084093,0.0002509854,0.000004325326,0.000009147027,0.0003667494,0.000006742601,0.003884781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0587123,"threshold_uncertainty_score":0.4259896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03728099889971924,"score_gpt":0.330638157646225,"score_spread":0.2933571587465057,"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."}}