{"id":"W2617745093","doi":"10.3968/9394","title":"Cybercrime and Poverty in Nigeria","year":2017,"lang":"en","type":"article","venue":"Canadian social science","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cybercrime; Poverty; The Internet; Government (linguistics); Nexus (standard); Internet privacy; Business; Economic growth; Political science; Law; Economics; Engineering; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0003934265,0.0001888956,0.0001858969,0.001272801,0.004666361,0.002491404,0.0001553437,0.0007099819,0.002889053],"category_scores_gemma":[0.0006294448,0.0001634544,0.0001244007,0.001058424,0.002551438,0.001742289,0.001662119,0.001055724,0.0001313345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002078157,"about_ca_system_score_gemma":0.0027733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01506882,"about_ca_topic_score_gemma":0.03325302,"domain_scores_codex":[0.9996895,0.0001300288,0.00001571121,0.00002069225,0.00004177703,0.0001022709],"domain_scores_gemma":[0.9995295,0.0001291631,0.0001841897,0.000006915344,0.00005415479,0.000096159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001574393,0.0005688845,0.3953111,0.001896901,0.00004223829,0.01351398,0.1722581,0.0008147169,0.001433477,0.2435132,0.01503504,0.1554549],"study_design_scores_gemma":[0.00001240125,0.000207109,0.2663252,0.006301833,0.00004921481,0.008761678,0.541956,0.0009393661,0.0006770067,0.04618439,0.1285247,0.00006116745],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8655717,0.03393784,0.000292286,0.0185627,0.0002520712,0.00003444503,0.00005201779,0.00000484666,0.08129207],"genre_scores_gemma":[0.9786276,0.0184092,0.0001044031,0.0004348257,0.0000254557,0.000008874389,0.000007719341,0.000001499135,0.002380472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01506882,"threshold_uncertainty_score":0.02996224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801609743680837,"score_gpt":0.2685725322895723,"score_spread":0.250556434852764,"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."}}