Public Sector Accounting in Europe: A Systematic Literature Review
Bibliographic record
Abstract
There has been an increased interest in public sector accounting research due to the changing standards worldwide with harmonization on the horizon. Despite this trend, there is still a lack of comprehension of national and local governments’ role in adopting and improving the international standards to enhance accountability, transparency and developing a more consistent and evolved society. This article aims to systematically review the literature on public accounting in European countries by examining the research trends.The analysis is developed through a bibliometric study based on three keywords: “Public Sector”, “Accounting”, and “Europe”. We systematically analyze the articles in the Web of Science database.This work presents several articles through a bibliometric study based on defined keywords. To define the relationship between the articles and the most cited authors, we based the systematic analysis on the aggregation of articles by clusters.In the last twenty years, public accounting in Europe has undergone significant changes, adapting to needs and innovations, which the most prominent resides in new accounting standards.Adopting International Public Sector Accounting Standards can benefit countries that introduce them into their system, namely increasing the system’s responsibility and transparency.Research is inevitably limited by the impact of public accounting changes in Europe and governments’ responses to social, economic and fiscal environments.This systematic review shed light on the challenges that public management has faced recently.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.040 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".