{"id":"W2530438507","doi":"","title":"Understanding The Nature of Out-of-School-In-School Technological Divide in Uganda","year":2016,"lang":"en","type":"article","venue":"","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Information and Communications Technology; Context (archaeology); Curriculum; ICTS; Presentation (obstetrics); Pedagogy; Sociology; Mathematics education; Psychology; Political science; Geography; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001697176,0.0002277549,0.0004530619,0.003854283,0.004973838,0.00870505,0.001090831,0.001121307,0.005238077],"category_scores_gemma":[0.00622617,0.0004744867,0.0002195159,0.002756808,0.004281458,0.00714187,0.007902494,0.001744223,0.0003178786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003102266,"about_ca_system_score_gemma":0.002585943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009280088,"about_ca_topic_score_gemma":0.020171,"domain_scores_codex":[0.9983272,0.0005553272,0.00008092519,0.00018582,0.0002178927,0.0006328037],"domain_scores_gemma":[0.9967154,0.001309072,0.0008725895,0.0001150795,0.0003607422,0.0006271696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00006991098,0.0001334759,0.1215588,0.0002611676,0.00001403216,0.001153978,0.8321382,0.0001058579,0.0007790225,0.01197857,0.0007851028,0.03102195],"study_design_scores_gemma":[0.000002945478,0.00004106652,0.1137429,0.0004185499,0.000008175302,0.0001640291,0.8721538,0.0001446294,0.0002608425,0.002230705,0.01081831,0.00001404229],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906992,0.000484655,0.0002383254,0.0007560168,0.00000758708,0.00001626881,0.00002778997,0.000004447985,0.007765718],"genre_scores_gemma":[0.9990928,0.0002122786,0.0000975435,0.00005683013,0.000002084853,0.00001764408,0.000009458997,0.000002337326,0.0005090901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009280088,"threshold_uncertainty_score":0.02250868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0464172068554621,"score_gpt":0.2892862624682125,"score_spread":0.2428690556127504,"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."}}