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Record W1584613819 · doi:10.1002/pad.1614

FLYING BLIND? EVIDENCE FOR GOOD GOVERNANCE PUBLIC MANAGEMENT REFORM AGENDAS, IMPLEMENTATION AND OUTCOMES IN LOW INCOME COUNTRIES

2012· article· en· W1584613819 on OpenAlexaff
Shaun Goldfinch, Karl DeRouen, Paulina Pospieszna

Bibliographic record

VenuePublic Administration and Development · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCorporate governanceService delivery frameworkGood governanceEmpirical evidencePublic sectorPublic economicsPublic serviceEconomicsNew public managementService (business)Public administrationPolitical scienceEconomic growthFinance

Abstract

fetched live from OpenAlex

SUMMARY Although considerable resources and attention have been allocated to recent ‘good governance’ public management reform in low income and fragile states, there is little evidence as to what degree this agenda has been implemented nor as to whether it has led to improved services and outcomes for populations. To address this lacuna, we conduct a review of the large but almost entirely qualitative literature on good governance reform in the 49 countries classed as low income by the United Nations. We find only a small number of documents that link good governance public sector reform agendas with implementation. Fewer still assess outcome. We conduct an empirical analysis of the relationship between reform agenda (using data from the literature review), implementation, service delivery and outcomes, as measured by performance on Millennium Development Goals indicators. We report that there is little, if any, empirical evidence that reform enhances service delivery. Copyright © 2012 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.230
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.230
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.012
Science and technology studies0.0020.006
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.000

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 head0.076
GPT teacher head0.378
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2012
Admission routes1
Has abstractyes

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