Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.040 |
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 it