SYSTEMATIC MAPPING OF PERFORMANCE ASSESSMENT RESEARCH: BIBLIOMETRIC STUDY WITH VOSVIEWER
Bibliographic record
Abstract
This article aims to find a map of the development of research on performance assessment. The study was conducted by searching through the Scopus database with the keyword performance assessment. The data from the search results are then analyzed descriptively based on the year of publication, the country that published the performance assessment research, and the focus of the research. To obtain a map of research development, the data from the Scopus database is exported into a Comma Separated Values (CSV) file format, then processed and analyzed using the VOSViewer application program to find out the bibliometric map of the development of performance assessment research. The results of the systematic mapping conducted show that the trend of performance assessment research publications indexed in Scopus from 2011 to 2020 has fluctuated. The trend based on the country that published the most articles was the United Kingdom with 77 articles. The research topic that is mostly done is the study of performance measurement with 32 articles. Then, through VOSViewer visualization, it shows that the map of the development of performance assessment research is divided into 5 clusters, namely; Cluster 1 consists of 6 research topics, namely assessment, efficiency, evaluation, performance, public service, and sustainability; Cluster 2 consists of 5 research topics, namely balanced scorecard, benchmarking, data envelopment analysis, performance assessment, and performance indicators; Cluster 3 consists of 5 research topics, namely accountability, Canada, governance, performance measurement, and public management; Cluster 4 consists of 4 research topics, namely local government, performance management, public administration, and public sector reform; Cluster 5 consists of 3 research topics, namely job satisfaction, performance evaluation, and the public sector.
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.
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 | 0.041 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.239 | 0.304 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".