An Empirical Study in Human Resource Management to Optimize Malaysian School Counselling Department
Bibliographic record
Abstract
This conceptual paper is to study the departmental improvement that needs to be implemented at Malaysian Schools Counselling Center by integrating Human Resources Management Practices. The study reviews literature on the Historical Background of Malaysian School Counselling Center and human resource management practices. The paper goes on to analyse factors and perceptions that is needed for revamping a systematic Counselling and Career Development Center in schools. Furthermore, its operational needs relevant human resource management approach which will contribute towards building the future human capital via the school systems. As human capital is the backbone of any country, it has become essential for any nation to produce the right human capital to ensure the workforce of the country is able to develop well balance country from political, economic and socially. However, there is rising challenges for the education sector to produce and feed the talents and various initiatives have been addressed in the Malaysian Education Blueprint 2013- 2025(MEB) by the Ministry of Education Malaysia. Hence, pilot study will be carried out at two governments secondary school in Malaysia located in an urban and a sub urban platform and to contribute at end of the research towards improvement in schools counselling center by using Human Resource Management approach. It is also aim that can support future studies can be carried out based on the practical implementation.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".