{"id":"W2023452315","doi":"10.12927/whp.2007.19376","title":"Structural Adjustment Programs and the Trickle-Down Effect: A Case Study of the Fujimori Period in Peru, Using Reproductive Health as an Indicator for Levels of Poverty","year":2007,"lang":"en","type":"article","venue":"World health & population","topic":"Global Health Care Issues","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Poverty; Reproductive health; Economic growth; Development economics; Infant mortality; Total fertility rate; Family planning; Structural adjustment; Child mortality; Fertility; Economics; Political science; Developing country; Population; Socioeconomics; Environmental health; Medicine","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.008737165,0.0002608288,0.0008883115,0.0002999546,0.001349657,0.000007501601,0.0001787599,0.0001159906,0.000009548783],"category_scores_gemma":[0.000431541,0.0001592624,0.00007789819,0.0009782038,0.0001212941,0.0001638688,0.0001150552,0.0005966018,5.841733e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387872,"about_ca_system_score_gemma":0.0008078247,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1551089,"about_ca_topic_score_gemma":0.1788542,"domain_scores_codex":[0.9928225,0.003227755,0.001966669,0.0005952346,0.00055187,0.000835924],"domain_scores_gemma":[0.996195,0.0006143979,0.001997992,0.000710523,0.0002283165,0.0002538006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002310058,0.0003492409,0.7968928,0.001997841,0.00003020196,0.000006363846,0.1536233,0.00006199606,0.000006397147,0.002024294,0.00007847368,0.04261907],"study_design_scores_gemma":[0.005853147,0.001901496,0.941965,0.0004554101,0.00003676695,0.00004806518,0.04849067,0.0005521227,0.000005166992,0.0004783834,0.00008672089,0.0001270217],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757302,0.001370454,0.00002666591,0.002917599,0.0009104498,0.01896008,0.00003910529,0.00003422608,0.00001116829],"genre_scores_gemma":[0.9973949,0.000009219217,0.0009122859,0.001071008,0.0002872923,0.0002369575,0.00002472,0.0000332714,0.00003038928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1450723,"threshold_uncertainty_score":0.9999505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08492193830150468,"score_gpt":0.4771709601193897,"score_spread":0.392249021817885,"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."}}