{"id":"W3025312308","doi":"10.17645/si.v8i2.2632","title":"Digital Inclusion Across the Americas and Caribbean","year":2020,"lang":"en","type":"article","venue":"Social Inclusion","topic":"Social Media and Politics","field":"Social Sciences","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agencia Nacional de Investigación e Innovación","keywords":"Inequality; Inclusion (mineral); Digital divide; Context (archaeology); Social inequality; Politics; Economic growth; Digital inclusion; Rural area; Situated; Financial inclusion; Political science; Geography; Development economics; Sociology; Social science; Economics; Information and Communications Technology; The Internet","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001072582,0.0003196418,0.0003250145,0.004485067,0.006617248,0.005973602,0.0004563798,0.0005918699,0.009352908],"category_scores_gemma":[0.004828938,0.00009732845,0.0002191422,0.007234644,0.001857129,0.002876167,0.008506504,0.0008883736,0.0002946423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003667876,"about_ca_system_score_gemma":0.005402727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2516598,"about_ca_topic_score_gemma":0.3424499,"domain_scores_codex":[0.9990185,0.0002744563,0.00003628952,0.00008553878,0.0001986749,0.0003864999],"domain_scores_gemma":[0.9982973,0.0004775879,0.0002243443,0.0001164881,0.0004287667,0.00045558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001328248,0.000182685,0.1162167,0.001109329,0.00007982043,0.002812673,0.2062972,0.0003682242,0.001418084,0.1548204,0.02158616,0.494976],"study_design_scores_gemma":[0.0000193057,0.00004842882,0.1718187,0.002312298,0.00009607516,0.0009715174,0.2844924,0.0004467363,0.0005521194,0.01760704,0.5215861,0.0000492422],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5293896,0.01834903,0.0005330815,0.0207526,0.0001844791,0.00007960002,0.0006443707,0.00005047617,0.4300168],"genre_scores_gemma":[0.9805458,0.011065,0.0003036454,0.001227822,0.00007300262,0.00007032856,0.0001594372,0.000018042,0.006536887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2516598,"threshold_uncertainty_score":0.5003901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03434097804282783,"score_gpt":0.3581337839248889,"score_spread":0.3237928058820611,"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."}}