Bibliographic record
Abstract
Malaysia is expected to become an ageing nation by the year 2020, when 10% of its population will fall within the age group 60 years and above.One of the challenges heralded by this development is income insecurity among the elderly population, who by virtue of their age are forced on retirement thereby making them to rely heavily on pension, social security or filial transfers, which in most cases are not sufficient to cater for their needs.Despite the fact that the elderly are "cash-poor", however, they are in most cases considered to be "asset-rich" by virtue of the possession of substantial wealth in form of housing equity that is trapped in their residential homes, which if released, can provide considerable income that can augment their existing source of income.Reverse mortgage offers the avenue where elderly people can access the trapped wealth in their residential home by releasing the housing equity therein in order to improve their welfare.This paper explores the prospects of developing reverse mortgage product market in Malaysia by presenting a SWOT analysis based on the synthesis of extant literature on the factors driving the demand and/or willingness of individuals to use the product as a source of supplementary income and consumption smoothing option in old-age.The analysis is expected to spur discussion on the subject as the country targets to become a high income economy by the year 2020 amidst increasing challenge of old-age poverty facing the elderly population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.950 | 0.941 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".