{"id":"W3175881321","doi":"10.55016/ojs/sppp.v8i1.42532","title":"Extractive Resource Governance: Creating Maximum Benefit for Countries","year":2015,"lang":"en","type":"article","venue":"The School of Public Policy Publications","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Weatherhead Center for International Affairs, Harvard University; Kementerian Energi Dan Sumber Daya Mineral; Foreign Affairs and International Trade Canada; University of Toronto; University of Cambridge; Princeton University; Australian Government; Commonwealth Scientific and Industrial Research Organisation; Harvard University","keywords":"Corporate governance; Resource (disambiguation); Business; Natural resource economics; Environmental economics; Environmental resource management; Environmental science; Computer science; Economics; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.01257731,0.0005059313,0.000615667,0.001251142,0.00366898,0.01042389,0.001568302,0.003586943,0.01034228],"category_scores_gemma":[0.01485753,0.0004484126,0.0005344541,0.001725354,0.008264376,0.01281479,0.02536412,0.003026679,0.001059494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003292293,"about_ca_system_score_gemma":0.008172145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139584,"about_ca_topic_score_gemma":0.002273967,"domain_scores_codex":[0.9910367,0.005383277,0.0002466801,0.0006879904,0.0009959344,0.00164944],"domain_scores_gemma":[0.9959413,0.001222256,0.0005036176,0.0009371722,0.0004747784,0.0009208609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005555616,0.00003807505,0.002033325,0.0002312276,0.00002333648,0.0001936609,0.004436772,0.002123495,0.0003195296,0.8966563,0.01835976,0.07552887],"study_design_scores_gemma":[0.00004859423,0.00007995361,0.00303874,0.0008542,0.00002365172,0.0001470123,0.008387542,0.001019767,0.0005855842,0.556559,0.4292307,0.0000252582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06614795,0.007486994,0.1246375,0.1226461,0.0009407457,0.0005749385,0.0002379181,0.0005106246,0.6768172],"genre_scores_gemma":[0.9320179,0.003161,0.02856761,0.009512362,0.000274608,0.0004465762,0.0001513944,0.0001287871,0.02573972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01257731,"threshold_uncertainty_score":0.06651598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0733343594104072,"score_gpt":0.2802652288649089,"score_spread":0.2069308694545017,"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."}}