TREN PENELITIAN TENTANG PENDIDIKAN ANTI KORUPSI DI PERGURUAN TINGGI DALAM PERSPEKTIF ANALISIS BIBLIOMETRIK
Bibliographic record
Abstract
This study examines publication trends on anti-corruption education in Indonesian universities using a bibliometric approach. Secondary data was obtained from scientific journals published between 2019-2024 and analyzed through software to extract metadata, manage references, and visualize conceptual links. The analysis has revealed a significant increase with a total of 989 publications and 9,167 citations, resulting in an average of 9.27 citations per publication. The academic impact index showed an h-index of 44 and a g-index of 75. The network visualization identified 6 main thematic clusters reflecting the shift from conceptual studies towards evaluating the actual implementation of anti-corruption education programs. The findings emphasize the strategic role of universities in building an academic culture of integrity and provide a basis for developing more effective anti-corruption education strategies and policies in the future.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".