Bibliographic record
Abstract
Low-cost housing plays a vital role in the development process especially in providing accommodation to those who are less fortunate and the lower income group. This effort is also a step in overcoming the squatter problem which could cripple the competitive drive of the local community especially in the state of Sabah, Malaysia. This article attempts to look into the influencing factors to low-cost housing in Sabah namely the government’s budget (allocation) for low cost housing projects and Sabah’s total population. At the same time, this study will attempt to show the implication from the development and economic crises which occurred during period 1971 to 2000 towards the provision of low cost houses in Sabah. Empirical analyses were conducted using the multiple linear regression method, stepwise and also the dummy variable approach in demonstrating the link. The empirical result shows that the government’s budget for low-cost housing is the main contributor to the provision of low-cost housing in Sabah. The empirical decision also suggests that economic growth namely Gross Domestic Product (GDP) did not provide a significant effect to the low-cost housing in Sabah. However, almost all major crises that have beset upon Malaysia’s economy caused a significant and consistent effect to the low-cost housing in Sabah especially the financial crisis which occurred in mid 1997.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".