{"id":"W1575479772","doi":"10.1080/02604027.2012.693854","title":"Preventing The Oil “Resource Curse” In Ghana: Lessons From Nigeria","year":2012,"lang":"en","type":"article","venue":"World Futures","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Resource curse; Nexus (standard); Leverage (statistics); Petroleum industry; Dutch disease; Oil boom; Revenue; Resource (disambiguation); Curse; Natural resource; Economics; Business; Natural resource economics; Economic growth; Development economics; Political science; Law; Sociology; Finance; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006247989,0.0001680908,0.0003040364,0.0001782878,0.0001858788,0.0000942264,0.0003625004,0.00006896588,0.001183642],"category_scores_gemma":[0.00005402481,0.0001428285,0.0001097796,0.0002495369,0.00003993649,0.0001308933,0.0001400659,0.000278324,0.0005272093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201075,"about_ca_system_score_gemma":0.000009427096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005927464,"about_ca_topic_score_gemma":0.001840082,"domain_scores_codex":[0.9986205,0.00002339544,0.0005108408,0.0002946476,0.00003510265,0.0005155653],"domain_scores_gemma":[0.9992308,0.0001126818,0.0002478858,0.0003110575,0.000004576438,0.00009302144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006330863,0.0002042625,0.7600477,0.00003235021,0.0001847587,0.000004852326,0.01337299,0.0001257833,0.00002558382,0.1179303,0.03924843,0.06875972],"study_design_scores_gemma":[0.0002643079,0.000003544641,0.1754133,0.00002411643,0.000003066637,9.001828e-7,0.001027633,0.00009207652,0.00004352066,0.002702175,0.8201896,0.0002357593],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8870224,0.02206257,0.00001390914,0.00523402,0.001353814,0.00009600168,0.00005482457,0.00002847447,0.08413399],"genre_scores_gemma":[0.9815084,0.0001077408,0.0004950346,0.001008528,0.0009204415,0.00003010792,0.00001878399,0.00002426644,0.01588668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7809411,"threshold_uncertainty_score":0.9997294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776061423419411,"score_gpt":0.2357337272960226,"score_spread":0.2079731130618284,"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."}}