Income Generation Activities among Academic Staffs at Malaysian Public Universities
Bibliographic record
Abstract
Income generation activities have been acquainted among public higher education institutions (HEIs) in Malaysia. Various factors that brought to insufficient of funding caused Higher Education Institutions(HEIs) to seek for additional income as to support the operation expenses. Financial sustainability issues made up the significant impact towards HEIs. Through the different instruments adopted by HEI, perhaps academic staffs are one of the parties that in charge of the income generation at universities. This research employed qualitative method by conducting interviews as a medium to provide insights to researcher. Then, the interviews data are analysed using the Interactive Model. The results pointed out that the main income generation activities originated from the research and consultancy whilst commercialization contributed the most significant income towards university. As a conclusion, the income which generated by the academic staffs is at upmost important to the development and sustainability of a university. Perhaps this research is significant to those who are concerning on the issues of income generating activities arisen among academic staffs. Through the results gained, certain parties may get known to the root of problems and then, solve it. It will eventually help the university to get a better way in order to attain the optimal results in income generation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".