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Record W2966754936 · doi:10.5430/afr.v8n3p149

Case Study in a Malaysian Public Agency on an Asset Management-Moving Towards the Accrual Basis of Accounting

2019· article· en· W2966754936 on OpenAlexvenueno aff
Sharifah Sabrina Syed Ali, Sharon Cheuk Choy Sheung, Mohd Waliuddin Mohd Razali

Bibliographic record

VenueAccounting and Finance Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
FundersUniversiti Malaysia Sarawak
KeywordsAccrualAccountingBusinessAsset (computer security)Order (exchange)Agency (philosophy)Government (linguistics)Management accountingGovernmental accountingAccounting information systemFixed assetFinanceRevenue recognitionProcess (computing)CashAccounting standardFinancial accountingFund accountingEconomics

Abstract

fetched live from OpenAlex

As part of the strategic reform of Malaysian public services under the Government Transformation Program (GTP), accrual accounting is expected to be fully adopted in public sector financial reporting commencing on 1 January 2015, in order to ensure alignment with the global accounting standards. Consequently, in order to access the government effectiveness of moving towards the accrual basis of accounting, this study is to examine the asset management system in a Malaysian public agency; to evaluate the extent of compliance with MPSAS 17, Property, Plant and Equipment (PPE), IPSAS 26, Impairment of Cash-Generating Assets and IPSAS 21, Impairment of Non-Cash Generating Assets. Using qualitative approach, a preliminary study was conducted via interviews and through obtaining documents. The findings include the following: MPSAS17 has not been strictly adhered to and software is used to monitor the assets; however, the disposal of assets is a manual process and is not automated. The study also discussed any weaknesses pertaining to the said asset accounting system, and suggested recommendations for improvement thereon.

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)0.015
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 score0.262
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.172
GPT teacher head0.459
Teacher spread0.286 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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