{"id":"W3161612294","doi":"10.2139/ssrn.3843318","title":"Estimating Chinese Bilateral Aid for Health: An Analysis of AidData’s Global Chinese Official Finance Dataset","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"International Development and Aid","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact","funders":"","keywords":"Finance; Economics; Actuarial science; Political science; Business","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":[0.002126844,0.0008975382,0.0008278255,0.004699115,0.0005766252,0.001217802,0.001012883,0.0006719265,0.003990896],"category_scores_gemma":[0.005911333,0.0003687798,0.0008033231,0.008254685,0.0004203453,0.0006510058,0.001375845,0.0006028909,0.001267474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002618141,"about_ca_system_score_gemma":0.005885649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3408868,"about_ca_topic_score_gemma":0.2574477,"domain_scores_codex":[0.9991525,0.0001538228,0.00006557252,0.0001601996,0.0002579716,0.0002098691],"domain_scores_gemma":[0.9976875,0.0006458635,0.0002728807,0.000353782,0.0008166886,0.0002233592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005139105,0.0002208125,0.7665148,0.0004640923,0.001032787,0.0004334041,0.0003598492,0.02269373,0.0005528398,0.004037777,0.1650522,0.03812383],"study_design_scores_gemma":[0.0001800636,0.00007678129,0.9029396,0.00007018298,0.0005164957,0.0001394981,0.0009593548,0.03724736,0.001010616,0.000851395,0.05592463,0.00008403857],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7420983,0.0009392382,0.001202751,0.0009463146,0.0001042379,0.0001088498,0.2492247,0.0003577817,0.005017875],"genre_scores_gemma":[0.6508172,0.0004319663,0.001576506,0.000162779,0.00006554562,0.0001909162,0.3433702,0.00005005439,0.003334871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3408868,"threshold_uncertainty_score":0.6778053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351564532632695,"score_gpt":0.3697417899604231,"score_spread":0.3562261446340962,"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."}}