Research trend on accountability and government performance: A bibliometric analysis approach
Bibliographic record
Abstract
Research aims: This research aims to analyze the trends, map the conceptual structure, and present the picture of research direction on accountability and government performance topics.Design/Methodology/Approach: A bibliometric analysis was used to obtain a structured overview and the research trend on accountability and government performance domains. The authors used the Scopus database from 1983 to 2022 and got 214 published documents, which were then analyzed with VOSviewer software and “Scopus Analyze Search Results.”Research findings: This study uncovered a significant increasing trend in the number of publications on accountability and government performance research, from two documents in 1985 to eighteen documents in 2022. The USA is the most productive country publishing on accountability and government performance research, followed by the United Kingdom, Indonesia, China, Australia, Canada, Germany, Netherlands, Spain, and Italy. The keywords that can be used for further research related to accountability and government performance are e-government, transparency, local government, governance approach, performance management, and corruption.Theoretical contribution/Originality: Based on the best of the authors’ knowledge, this is the first paper that analyses the research trend on accountability and government performance with the use of bibliometric analysis. In addition, for exploring and analyzing large volumes of scientific research, the use of bibliometric analysis is a popular and rigorous method.Practitioner/Policy implication: The use of bibliometric analysis is essential to identify research gaps and look for themes or terms and become a potential direction to explore the relationship of each term.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.132 | 0.219 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".