{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003788922,0.00006980835,0.00009302608,0.0001053213,0.002037818,0.0005562483,0.001133182,0.00002854885,0.00001107415],"category_scores_gemma":[0.0001224205,0.00006808084,0.00001593831,0.0001942365,0.0005526138,0.0009052961,0.0002915553,0.0000652108,0.00001737323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001978697,"about_ca_system_score_gemma":0.0003263722,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1302557,"about_ca_topic_score_gemma":0.4252166,"domain_scores_codex":[0.9990063,0.000009086513,0.00008586841,0.0002827738,0.0001795839,0.0004363837],"domain_scores_gemma":[0.9993909,0.00001158002,0.0000299412,0.0003042452,0.00003804839,0.0002252739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000001788341,0.00001185454,0.1695901,0.000005720152,0.00000544674,0.00006277965,0.008602413,1.685266e-7,0.0006989566,0.7418056,0.008100438,0.07111476],"study_design_scores_gemma":[0.0002677046,0.00001722505,0.951168,0.00001040981,0.00000137859,0.000002954105,0.0003915344,0.0001069886,0.0002975919,0.004410305,0.0430626,0.0002632948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1325395,0.00004721709,0.0002035205,0.003854461,0.0004355759,0.0001193104,0.000003751346,0.00002483128,0.8627719],"genre_scores_gemma":[0.9980351,0.000008721304,0.0001899695,0.001291791,0.00004757458,0.000003790669,1.085855e-7,0.00000191021,0.000420991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8654957,"threshold_uncertainty_score":0.9992614,"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."}}