{"id":"W3124478482","doi":"10.3386/w15705","title":"The Political Resource Curse","year":2010,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Resource curse; Politics; Resource (disambiguation); Political science; Computer science; Law; Computer network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001769491,0.0003288243,0.0007822064,0.001258943,0.002298159,0.003297654,0.0004293249,0.001285801,0.03106439],"category_scores_gemma":[0.01138233,0.0002448939,0.0004260021,0.00196449,0.002439843,0.001708541,0.002257416,0.002516703,0.002628626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002981829,"about_ca_system_score_gemma":0.004856249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01312954,"about_ca_topic_score_gemma":0.01937165,"domain_scores_codex":[0.9983214,0.0005888666,0.00004089053,0.0002324693,0.0003991933,0.0004171793],"domain_scores_gemma":[0.986948,0.006787898,0.003562784,0.0007616645,0.0006611792,0.001278433],"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":[0.000111761,0.0002239808,0.03725255,0.0002755626,0.0001541197,0.0005950424,0.001912216,0.005534375,0.0005112237,0.8244151,0.06156226,0.06745188],"study_design_scores_gemma":[0.0001139313,0.0001897151,0.07457262,0.0004233299,0.0001516512,0.0006064929,0.002631509,0.00712203,0.001048342,0.3652304,0.5478458,0.00006399946],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2435014,0.01015271,0.01642316,0.08370338,0.0005453856,0.0001260763,0.001664059,0.0003688081,0.6435151],"genre_scores_gemma":[0.9242731,0.003309726,0.001081403,0.004251558,0.000446119,0.00006139365,0.0002553246,0.0001018468,0.06621959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03106439,"threshold_uncertainty_score":0.1039208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3797150897540399,"score_gpt":0.4813523443540378,"score_spread":0.1016372545999978,"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."}}