Retirement planning confidence of private sector employees in Kuantan, Pahang / Adibah Athirah A.Razak
Bibliographic record
Abstract
Malaysia is one of the developing country which also can stands together with other developed country in this world. However, it is quite disappointing when according to Employment Provident Fund (EPF), there were only 22 percent from 6.7 million active contributors have enough funds to retire. Moreover, it is also stated that one of the cause of this situation happens is because of Malaysia's low salary structure. This can be strengthen by 89 percent of the working population earns below than RM 5,000. This is a serious problem to be catered and everyone must do something in order to secure their retirement life. According to previous research conducted, it is proven that private sector employees were confident pertaining to retirement planning as they are financial literate, have clear goals and their positive attitude towards retirement. Thus, this study was conducted to discover whether private sector employees in Kuantan, Pahang are confident about their retirement planning. This study had been using retirement planning confidence of private sector employees as dependent variable, while for independent variables are financial literacy, goal clarity and attitude towards retirement. The results from this study shows that all independent variables are accepted which are the p value is below than 0.05. Hence, all independent variables in this study which are financial literacy, goal clarity and attitude towards retirement are significant and can be conclude that private sector employees in Kuantan, Pahang are confident regarding their retirement planning.
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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) | 0.003 | 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".