Current Practices and Issues in the Accounting for Government Infrastructure Assets
Bibliographic record
Abstract
Purpose: This paper aims to explore the current accounting practices and issues in accounting for infrastructure assets in Malaysian public sector organisations. Comparatively, this paper also provides an overview of current practices in other countries. Design/ Methodology/ Approach: Data collection was through reviews of extant literature, accounting standards and guidelines by the Accountant General’s Department of Malaysia (AGD), including guidelines and government publications in other countries. Findings: Accounting practice for infrastructure assets varies across countries. There is no specific accounting standard for infrastructure assets. Instead, most countries including Australia, Austria, Canada, France, New Zealand and Malaysia follow the accounting standards for property, plant and equipment to account for infrastructure assets. However, given the unique nature of infrastructure assets, there are issues in identifying what constitute infrastructure assets and determining which government organisations have control over their assets. In addition, challenges in recognition and valuation of infrastructure assets, which include initial and subsequent measurement, depreciation and disclosure also exist. Practical Implications: The government’s spending for infrastructure assets represents a significant proportion of its budget. Therefore, accurate accounting treatments and reporting of the assets is pivotal. This paper aimsto present an update to policy makers and practitioners on current pertinent accounting standards, issues and accounting practices at the international level as well as at the Malaysian federal government. Originality/ Value: Research on accounting for infrastructure assets is still limited. Findings from this research will add to the body of knowledge by highlighting the main issues and providing basis for further investigation. Keywords: Infrastructure assets, definition, recognition, IPSAS17, MPSAS17.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".