New development: The development of standardized charts of accounts in public sector accounting
Bibliographic record
Abstract
IMPACTCharts of Accounts (CoAs) in the public sector are important to control accounting records. They support the preparation of accurate and reliable financial statements and consolidated reporting. Standardized CoAs at the national level are desirable but specificities of different public sector areas must be considered, as well as harmonization with budget and Government Finance Statistics (GFS) classifications. Having broad international guidance for each country to develop its own CoA, while fostering public sector financial reporting harmonization, would allow for improved comparability of fiscal effects during difficult periods, such as the Covid 19 pandemic.ABSTRACTThis article addresses the development of standardized Charts of Accounts (CoAs) in public sector accounting and reporting. In particular, it focuses on matters concerning the role CoAs have, or should have, at a national level, their main technicalities and the expected impact of using them as a bookkeeping instrument on the accuracy of accounting records and, ultimately, on the reliability and usability of the financial information for different purposes. Empirical evidence is provided from a survey to representatives of accounting international and national (Belgium, Brazil, Estonia and Portugal) standard-setters and preparers.
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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 | 0.038 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".