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Record W2046234280 · doi:10.5539/ijms.v6n6p104

Service Quality and Performance of Public Sector: Study on Immigration Office in Indonesia

2014· article· en· W2046234280 on OpenAlexvenueno aff
Ernani Hadiyati

Bibliographic record

VenueInternational Journal of Marketing Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessService qualityMarketingQuality (philosophy)ImmigrationPublic sectorService (business)Tertiary sector of the economyEconomicsPolitical scienceEconomy

Abstract

fetched live from OpenAlex

The objectives of the research are to describe the public sector service form delivered to consumers/citizen, to find out the consumers’ satisfaction on public sector service, and to discover the public sector quality and performance delivered to consumer/people. The research is to measure the level of consumers’ satisfaction in using the public sector services through government policy approach towards the service satisfaction, and people’s judgment towards the quality and performance served by public service administrator apparatus. This research results in, first, description of the public sector service form delivered to consumers/people as the public sector service users, second, measuring the consumers or people’s satisfaction based on the public sector service satisfaction measurement indicator in reference to government’s decrees and laws, and third, determining the public sector service performance and quality applied to the consumers/people. The result of the research is of benefit for the government’s consideration as public sector service administrator for consumers/people in the effort to ameliorate the service performance and quality.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.068
GPT teacher head0.324
Teacher spread0.255 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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