Bibliographic record
Abstract
The purpose of this study is to analyze current status of the governmental FMISs used in a few representative OECD countries and to suggest ways to improve Korea’s FMIS. Korea’s central government is currently using the dBrain system (digital budget and accounting system) as the FMIS. As of 2018, the dBrain system has been in operation for over 10 years since its establishment. Up until now there has been very limited research on the FMISs operated in OECD countries. Previous research just briefly outlined each country''s system in a superficial way. Therefore, this study analyzes the current status of the FMISs of Sweden, the United States, United Kingdom, and Canada in many ways. We choose the four countries which are known for being innovative in fiscal reform and FMIS. The study analyzes eight characteristics of the four OECD countries’ FMISs: the general characteristics, whether the FMIS is the integrated/discentralized system, the public financial coverage, the operating organization, the system configuration, the strengths, the weaknesses, and the information disclosed by the FMIS. This study can contribute to establish future improvement direction of the Korean dBrain system by the analysis of the OECD country FMISs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".