{"id":"W1824047393","doi":"10.3386/w18539","title":"The Political Economy of Government Revenues in Post-Conflict Resource-Rich Africa: Liberia and Sierra Leone","year":2012,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Université Laval","keywords":"Sierra leone; Government (linguistics); Politics; Political science; Revenue; Development economics; Resource (disambiguation); Geography; Government revenue; Economy; Political economy; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009800354,0.0001494655,0.000282774,0.0009432731,0.001691758,0.005047763,0.0003999555,0.00072798,0.004041454],"category_scores_gemma":[0.003666233,0.0002089426,0.0001774946,0.0007679781,0.002826279,0.001498522,0.001879773,0.001473526,0.0001776838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003756702,"about_ca_system_score_gemma":0.001899635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02114728,"about_ca_topic_score_gemma":0.03051619,"domain_scores_codex":[0.9994343,0.0001250794,0.000009607354,0.00001990682,0.0000427835,0.0003682199],"domain_scores_gemma":[0.9983395,0.0006101876,0.0006561252,0.00004245407,0.00008661216,0.0002650365],"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":[0.0009225687,0.0004677503,0.1310945,0.0001501923,0.0001303475,0.005576891,0.007145845,0.01666671,0.002088209,0.7933637,0.006252914,0.03614033],"study_design_scores_gemma":[0.0009550935,0.0005075159,0.5266147,0.0005600256,0.0002964868,0.001577467,0.06498607,0.05410251,0.005012273,0.265844,0.07937486,0.0001691104],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684723,0.0004596423,0.0001898143,0.003104672,0.000008407474,0.00001483244,0.00005025608,0.000004973772,0.02769505],"genre_scores_gemma":[0.9984834,0.0002079343,0.00003115865,0.00007444011,0.00000586209,0.000004377157,0.00001156365,0.00000149552,0.001179782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02114728,"threshold_uncertainty_score":0.04204839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2823539084703963,"score_gpt":0.4053383204020298,"score_spread":0.1229844119316335,"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."}}