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Record W2904642642 · doi:10.1017/lap.2018.59

States and Capitals of Health: Multilevel Health Governance in Brazil

2018· article· en· W2904642642 on OpenAlexaff
Jorge Antonio Alves, Christopher L. Gibson

Bibliographic record

VenueLatin American Politics and Society · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCorporate governanceState (computer science)Service delivery frameworkCitizen journalismPublic administrationCapital (architecture)Economic growthPrimary carePublic healthHealth careCapital cityPopulationService (business)BusinessPolitical scienceEnvironmental healthGeographyEconomicsMedicineNursingFinanceEconomic geography

Abstract

fetched live from OpenAlex

Abstract Scholars of Brazil’s public health system commonly note the intertwined roles that federal, state, and municipal governments play in delivering care, yet few studies systematically examine varying service performance in areas with overlapping mandates, such as state capitals. This study addresses that gap by developing and analyzing a novel measure of municipal primary care provision that accounts for the proportion of the population without access to private services in 11 large capital cities, then comparing them to the noncapital municipalities in their states. The study finds that capitals generally underperform the noncapital municipalities in primary service delivery. It then draws on a comparative case study in two major capitals, Salvador and Belo Horizonte, and their encompassing states to explore how a history of cooperative or adversarial relations between state and local governments conditions the impact of partisanship, participatory institutions, and public health activists on primary care delivery.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.324
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2018
Admission routes1
Has abstractyes

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