{"id":"W4205466294","doi":"10.1002/pa.2811","title":"Strengthening state capacity in Africa: Lessons from the Washington versus Beijing Consensus","year":2022,"lang":"en","type":"article","venue":"Journal of Public Affairs","topic":"International Development and Aid","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Washington Consensus; Industrialisation; Beijing; State (computer science); CLARITY; Frontier; Scope (computer science); Order (exchange); Position (finance); Political science; China; Development economics; Economic system; Economic growth; Economics; Law; Politics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003868322,0.0002883837,0.00022995,0.000948596,0.002796368,0.002503155,0.0004767503,0.00150865,0.002013902],"category_scores_gemma":[0.005033631,0.0001274591,0.0002714373,0.0008983244,0.008435765,0.005618448,0.003703273,0.00210238,0.0001182991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003764773,"about_ca_system_score_gemma":0.004332343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005415482,"about_ca_topic_score_gemma":0.01243945,"domain_scores_codex":[0.9985732,0.0009190298,0.00004459689,0.00008377028,0.0001450033,0.000234355],"domain_scores_gemma":[0.998229,0.001115665,0.000162842,0.0001157405,0.0002079482,0.0001689283],"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":[0.00001147218,0.00001337328,0.0007328565,0.00004653115,0.000005338235,0.0001351576,0.003961728,0.001221274,0.0001506968,0.9700378,0.0008630203,0.02282074],"study_design_scores_gemma":[0.00002565309,0.00007736175,0.003261601,0.0003956804,0.00002916203,0.0001459285,0.01855719,0.002867284,0.0008375768,0.8871719,0.08659706,0.00003366852],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3008969,0.009372164,0.0359058,0.1287151,0.0005574418,0.0001120819,0.00002439649,0.00004711229,0.5243691],"genre_scores_gemma":[0.9903395,0.001957322,0.002169078,0.0008617014,0.00003764489,0.00002468579,0.000003825743,0.000005249782,0.004601045],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005415482,"threshold_uncertainty_score":0.0273155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1257332392773863,"score_gpt":0.306154035559951,"score_spread":0.1804207962825647,"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."}}