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Record W1973337133 · doi:10.5539/ass.v7n2p25

Revenue Mobilisation Performance of Union Parishad in Bangladesh: Is It Convergence of Central-Local Relations?

2011· article· en· W1973337133 on OpenAlexvenueno aff
Md. Anwar Ullah, Soparth Pongquan

Bibliographic record

VenueAsian Social Science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueLocal governmentLocal governanceCorporate governanceConvergence (economics)BusinessCentral governmentEconomicsPublic economicsPublic administrationFinanceEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Despite several local revenue sources, Union Parishads (Councils) in Bangladesh are yet to perform optimal local revenue mobilisation. This paper evaluates Unions revenue trend and performance, budget and planning practices, effect of central grants on local revenue in the backdrop of central government recent initiative for Unions capacity building. Analysis includes three representative Unions, in a comparative perspective, and uses secondary and primary data (from Parishad functionaries, local citizen, government officials and national experts). Findings show that open budget discussion, discretionary and performance grants have positive impact on local revenue collection while this study raises question about sustainability of the revenue augmentation due to disparity of deconcentrated allocations system and feeble local democratic governance. Study recommends making adjustments in local revenue shares, increase discretionary grants, and validation of local participatory governance. This study has implications for local revenue mobilisation through convergence of central-local policy and strategy, specially for developing countries having similar local socio-economic and revenue source footings.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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