{"id":"W3204887615","doi":"10.1017/s0003055421000927","title":"Benevolent Policies: Bureaucratic Politics and the International Dimensions of Social Policy Expansion","year":2021,"lang":"en","type":"article","venue":"American Political Science Review","topic":"Social Policy and Reform Studies","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada; Weatherhead Center for International Affairs, Harvard University; Hospital for Sick Children; University of Toronto; International Development Research Centre; Fulbright Canada; McMaster University; Harvard T.H. Chan School of Public Health; American Political Science Association","keywords":"Technocracy; Bureaucracy; Government (linguistics); Politics; Incentive; Population; Social policy; Political science; Public policy; Political economy; Public administration; Economic growth; Economics; Sociology; Market economy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001290176,0.0001210335,0.000448496,0.00008022149,0.001418794,0.00006008915,0.0004376195,0.00003069904,0.00002418083],"category_scores_gemma":[0.005543947,0.000067334,0.0001523677,0.001573062,0.01673605,0.0001342014,0.0003453776,0.0001280412,0.000006933867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002548463,"about_ca_system_score_gemma":0.001708673,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03185135,"about_ca_topic_score_gemma":0.0003889286,"domain_scores_codex":[0.9973516,0.0002875222,0.0003706697,0.000242974,0.0008910959,0.0008561191],"domain_scores_gemma":[0.9985128,0.0003941891,0.0001634905,0.0001797428,0.0004109184,0.0003388769],"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":[0.000001460807,0.00002956326,0.0000907525,0.0000465747,0.00001555519,8.311333e-7,0.003189842,4.097315e-8,0.00004561278,0.9897879,0.0003716021,0.006420311],"study_design_scores_gemma":[0.001298611,0.0001555103,0.03443269,0.00195837,0.0005292734,0.00004760491,0.1218135,0.0000589664,0.0006866239,0.6363412,0.2016784,0.0009992472],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05747678,0.005884443,0.0000155625,0.8412808,0.0003122267,0.0004822065,0.00003668141,0.00004879337,0.09446253],"genre_scores_gemma":[0.9333242,0.04666543,0.00009798061,0.01875958,0.0007099644,0.00001917286,0.000001223654,0.000006092881,0.0004163816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8758474,"threshold_uncertainty_score":0.9998812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02958646072284666,"score_gpt":0.4065165858813068,"score_spread":0.3769301251584602,"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."}}