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Record W4394570472 · doi:10.1108/pap-06-2023-0090

Electronic public service delivery: progress and challenges in Bangladesh

2024· article· en· W4394570472 on OpenAlexaff
Ahmed Shafiqul Huque, Jannatul Ferdous

Bibliographic record

VenuePublic Administration and Policy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsMcMaster University
Fundersnot available
KeywordsService delivery frameworkCompetence (human resources)BusinessOriginalityGovernment (linguistics)Public relationsService (business)Public servicePublic valueLocal governmentMarketingPublic administrationEconomicsPolitical scienceManagement

Abstract

fetched live from OpenAlex

Purpose The paper aims to examine the state of electronic service delivery in Bangladesh. It reviews the structure and operation of the “e-service” centers at the district, sub-district (upazila), and union levels by taking an inventory and assessing their contributions. Design/methodology/approach The paper is based on a review of the functions and operations of the service delivery agencies with reference to the claims made by the government. It is based on secondary materials obtained from academic studies, government documents, relevant websites, and media reports. Findings Electronic delivery of public services in Bangladesh has not been effective as planned. There are issues regarding channels of communication, the competence of public officials, human and financial resources, and political will to support the agencies delivering public services. Originality/value The paper examines the arrangements, practices, and problems of delivery of public services in Bangladesh through e-service centers at the local levels to determine the progress and potentials of employing digital technology for addressing problems. It proposes the strategy for public service delivery by using digital technology in the country.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.961

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 studies0.0000.000
Scholarly communication0.0010.001
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 head0.063
GPT teacher head0.336
Teacher spread0.273 · 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 designNot applicable
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

Citations6
Published2024
Admission routes1
Has abstractyes

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