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Record W2023452315 · doi:10.12927/whp.2007.19376

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

2007· article· en· W2023452315 on OpenAlexvenueno aff
Sonia Menon

Bibliographic record

VenueWorld health & population · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyReproductive healthEconomic growthDevelopment economicsInfant mortalityTotal fertility rateFamily planningStructural adjustmentChild mortalityFertilityEconomicsPolitical scienceDeveloping countryPopulationSocioeconomicsEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

The purpose of this analysis is to investigate whether the Organisation for Economic Co-operation and Development/United Nations/World Bank (OECD/UN/WB) poverty reduction objectives are compatible with the neo-liberal development model, using Peru as a case study. Three OECD/UN reproductive health indicators were selected to assess poverty: female literacy, infant mortality and maternal mortality. Fertility rates were also analyzed to explore the impact that neo-Malthusian policies have wielded. Shortly after his ascendance to power in 1990, President Fujimori undertook health finance reforms to promote cost-effectiveness and efficiency under political guidance from international financial institutions (IFIs). Internationally, Peru was portrayed as a neo-liberal success story. However, maternal mortality rates throw into contention claims that economic growth has a trickle-down effect. From the fertility rates, it can be deduced that the advent of structural adjustment has led to a resurgence of a neo-Malthusianism approach, putting family planning on the front burner, to the detriment of reproductive health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.085
GPT teacher head0.477
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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