Adoption of International Public Sector Accounting Standards in Public Sector of Developing Economies -Analysis of Five South Asian Countries
Bibliographic record
Abstract
We examined the extent of adoption of the International Public Sector Accounting Standards (IPSAS) in South Asia and the challenges that are decelerating this process. The moderating organization, International Public Sector Accounting Standards Board (IPSAB), instituted IPSAS in an effort to improve financial reporting by public sector organizations and for comparability purposes. The aims of this research were to establish to what extent the IPSAS has been adopted in South Asia and determine the drawbacks contributing to its slow adoption process. To answer the study questions a literature review of the South Asian countries that have adopted the IPSAS was conducted. The study findings show that most of the South Asian nations have adopted the IPSASs though to different extents. Nepal, Bangladesh, Pakistan and Sri Lanka have implemented the IPSAS but taking different approaches and directions, while India still uses the cash based accounting system. The key barriers include; lack of experienced staff, delay in provision of information by the public entities, and lack of a defined implementation timeframe which seem to cut across these countries.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".