Retirement Planning Behaviour of Working Individuals and Legal Proposition for New Pension System in Malaysia
Bibliographic record
Abstract
<p class="MsoNormal" style="text-align: justify;">Problems have been identified pertaining to retirement scheme of the private sector employees in Malaysia where there is no legislated pension system in force. As a result of that, pension scheme and savings are more of a voluntary basis; although the principle is good but in practice many retirees suffer financially during their retirement. The objectives of this study are to examine factors contributing to individual’s retirement planning behavior and the private pension system in the private sector in Malaysia. Retirement planning behaviour in this study was measured with series of questions on behaviour about retirement planning. A total of 500 working individuals from private sectors in the age group of 40 years and above had participated in this study. The results identified several significant variables in the prediction of retirement planning among working individuals in Malaysia, including individual who had higher levels of education, higher levels of income, financial literacy, retirement goal clarity and attitude towards retirement. There is a correlation between retirement planning behavior and saving for old aged. As a response to the result collected from the survey, a legal proposition is put forward to address issues of pension during retirement among private sector’s employees.</p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".