{"id":"W3097990861","doi":"10.1002/pa.2543","title":"<scp>USA–China</scp> trade war: Economic impact on Indonesia","year":2020,"lang":"en","type":"article","venue":"Journal of Public Affairs","topic":"Global Financial Crisis and Policies","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Depreciation (economics); China; Balance of trade; Commodity; Quarter (Canadian coin); Exchange rate; International economics; Indonesian; Terms of trade; Investment (military); Monetary economics; Economy; International trade; Market economy; Capital formation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006994403,0.0002674873,0.0007886716,0.0003437439,0.0001023368,0.0001833299,0.0005684556,0.0001630106,0.00031288],"category_scores_gemma":[0.000322027,0.0002419299,0.0006122376,0.0002998727,0.00006057473,0.0006408383,0.00005605417,0.0004473538,0.0007949407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003838215,"about_ca_system_score_gemma":0.0001750304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009238733,"about_ca_topic_score_gemma":0.0000107694,"domain_scores_codex":[0.9980108,0.00003842983,0.001099978,0.0002548662,0.00008899224,0.0005069685],"domain_scores_gemma":[0.9979837,0.0000960385,0.001167963,0.000213351,0.00002897783,0.0005099757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005127558,0.0002205828,0.1610392,0.00003664294,0.0003999489,0.00006145799,0.003854841,0.003308622,0.00004594403,0.3246637,0.5038569,0.002460946],"study_design_scores_gemma":[0.001383549,0.001525183,0.4244947,0.00002133993,0.00002142981,0.00007572403,0.001232072,0.0009254347,0.0001042251,0.006754958,0.5632277,0.0002336368],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248206,0.001706259,0.0003731721,0.01104945,0.000731963,0.0001077559,0.0002100482,0.00002120252,0.06097956],"genre_scores_gemma":[0.9973971,0.0004474149,0.0001005015,0.0007634762,0.001150164,0.000001960236,0.000004405944,0.00003298143,0.0001020119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3179087,"threshold_uncertainty_score":0.9999831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03141020081180203,"score_gpt":0.2409017936451018,"score_spread":0.2094915928332998,"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."}}