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Record W607985963

Public service delivery in India : understanding the reform process

2010· preprint· en· W607985963 on OpenAlexaboutno aff
Vikram K. Chand

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsLaggingPoliticsCorporate governanceState (computer science)Agency (philosophy)Service delivery frameworkWest bengalPublic administrationEconomic growthPower (physics)Political scienceCivil servicePublic serviceService (business)BusinessEconomyEconomicsSociologySocioeconomicsFinanceSocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

This collection looks at processes of change and reform in public service delivery in a range of states and sectors, and over time spans. The first three essays examine reforms that have improved prospects for economic growth and poverty alleviation in Bihar; improved the functioning of public sector enterprises and the power sector, and initiated improvements in education in West Bengal; and the efficient delivery of economic services in Gujarat in order to pursue a high-growth agenda. The next two essays focus on regulation in infrastructure as well as the delivery of urban services. The question of balancing greater autonomy with accountability to improve public service delivery through the use of executive agencies is also analysed. The final essay discusses how India might absorb lessons for the effective implementation of the Right to Information Act (2005) from countries such as Mexico, South Africa, and Canada. The volume shows how reform is an ongoing process that depends critically on contextual factors. These include the history of reform ideas, the capacity of the state to execute reform, and the nature of the state itself and its relationships with key actors, such as the private sector and unions.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.546
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.003
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 head0.144
GPT teacher head0.347
Teacher spread0.203 · 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 designQualitative
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
Published2010
Admission routes1
Has abstractyes

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