{"id":"W3199917302","doi":"10.5539/ijef.v13n10p157","title":"Does Human Capital Mitigate Resource Curse? Evidence in the Short- and Long-Run","year":2021,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Natural Resources and Economic Development","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human capital; Distributed lag; Economics; Natural resource; Curse; Term (time); Resource curse; Natural capital; Error correction model; Capital (architecture); Autoregressive model; Econometrics; Development economics; Cointegration; Economic growth; Geography; Biology; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0007253073,0.0001227902,0.0002994219,0.0001404895,0.00006879395,0.0002445168,0.0003718768,0.00006179525,0.00004883872],"category_scores_gemma":[0.0000900616,0.00009272808,0.00008457261,0.00004850119,0.00009083662,0.0003911123,0.0001141515,0.0002262907,0.000006868554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009480229,"about_ca_system_score_gemma":0.00003343305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003111361,"about_ca_topic_score_gemma":0.0001564408,"domain_scores_codex":[0.9987402,0.00001541264,0.000787734,0.0002574378,0.00003272348,0.0001665081],"domain_scores_gemma":[0.9992505,0.000114923,0.0004064453,0.0001301801,0.00005696746,0.00004096656],"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":[0.0001343768,0.0001929267,0.4001667,0.00003477633,0.0003017758,0.0004962459,0.004371128,0.00151867,0.00003736267,0.5648703,0.0008470928,0.02702862],"study_design_scores_gemma":[0.001302175,0.0001238962,0.533065,0.000301655,0.00001221353,0.0006700922,0.0008588262,0.001844864,0.0002047694,0.1457171,0.315391,0.0005085226],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808648,0.01059678,0.00001761246,0.007126714,0.0005542865,0.00005301223,0.00002895925,0.000001340292,0.0007564902],"genre_scores_gemma":[0.9796101,0.01857936,0.0002562002,0.0008897459,0.0002258655,0.000003185823,0.000003856072,0.000009299437,0.0004223803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4191532,"threshold_uncertainty_score":0.3781341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0348407307503104,"score_gpt":0.247045640906198,"score_spread":0.2122049101558877,"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."}}