Good Governance and Integrity: Academic Institution Perspective
Bibliographic record
Abstract
Integrity is one of the moral principles related to moral uprightness. Recently, there are a lot of issues discussed regarding the integrity in public sector administration especially in public sector. Currently governance in public administration has been exposed to public criticism due to the governance failure, fraud, corruption and poor internal control. The purpose of the study is to examine the relationship between factors of good governance and the practice of integrity in academic institution. The factors of good governance include ethical leadership, financial resources and asset management. The study was carried out by using questionnaire and simple random sampling was chosen. The questionnaire survey was distributed to 98 academics from two academic institutions in Malaysia. Such sample was chosen since this study was focused on the academic’s perspective on integrity practice in academic institutions and none of the research has been done in term of good governance and integrity in academic institutions Malaysia. This study found that all three factors of good governance which are ethical leadership, financial resources and asset management have significant relationship on integrity practice in academic institution. The findings of this study can assist academic institutions in Malaysia to improve their governance system and also code of ethics in their organization. In order to improve future studies, it is recommended that the data collection made to be more extensive. This can help in observing the variation of practice of good governance and integrity in academic institutions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".