{"id":"W2965954933","doi":"10.1080/02681102.2019.1650244","title":"Data inequalities and why they matter for development","year":2019,"lang":"en","type":"article","venue":"Information Technology for Development","topic":"ICT in Developing Communities","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Conceptualization; Big data; Data science; Inequality; Conversation; Sociology; Harm; Data governance; Computer science; Political science; Data quality; Business; Law; Marketing; Data mining","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":[0.01749831,0.0003615132,0.0005680697,0.003197602,0.007370875,0.01394546,0.001218366,0.002390933,0.01079836],"category_scores_gemma":[0.05805397,0.000365208,0.0003963658,0.004177423,0.02908106,0.0206658,0.01176978,0.005596356,0.0008638885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01087481,"about_ca_system_score_gemma":0.009897891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01885451,"about_ca_topic_score_gemma":0.011216,"domain_scores_codex":[0.9804243,0.009726298,0.00081976,0.001745411,0.003803913,0.003480229],"domain_scores_gemma":[0.955215,0.03167983,0.003231942,0.00238721,0.004531858,0.002954256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003607589,0.00002654509,0.009090946,0.0001934112,0.00001734744,0.0001544669,0.01856756,0.0001576773,0.0000990231,0.9368823,0.007097152,0.02767757],"study_design_scores_gemma":[0.00002437647,0.00002548833,0.008535056,0.001639551,0.00003078987,0.0002412072,0.04850634,0.0004624234,0.0005298991,0.7534615,0.1865088,0.00003458887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1319346,0.02322747,0.01193864,0.5627871,0.001248252,0.0001105194,0.001100736,0.00009178606,0.2675609],"genre_scores_gemma":[0.9721176,0.007698355,0.002116708,0.01204052,0.0003415229,0.0001294267,0.0002470057,0.0000809392,0.005227991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01885451,"threshold_uncertainty_score":0.09254104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04439011704528152,"score_gpt":0.2717615700022367,"score_spread":0.2273714529569552,"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."}}