Boosting Homeownership Affordability for Low-Income Communities in Indonesia
Bibliographic record
Abstract
This study aims to determine the effect of inflation, regional minimum wages, and subsidized housing costs on the purchasing power of low-income communities needing to own a home as an intervening variable.This study uses the Structural Equation Modeling (SEM) method to analyze the data from the districts of East Java, Indonesia.Broadly speaking, this study found that the higher the need to own a home, the higher the purchasing power of Low-Income Communities because the population is increasing, so the need to own a place to live is also directly proportional.The practical implication of this study is that it can contribute useful information relating to the effect of inflation, regional minimum wages, and subsidized house prices on the purchasing power of low-income communities with a need to own a house as an intervening variable.Therefore, the government needs to control the impact caused by inflation in the form of rising bank interest rates by subsidizing it so that it remains stable.In the future, the government should include housing allowances in the minimum wage component.The government should not only subsidize house prices, but also production costs, so that the price of subsidized houses remains stable.
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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 | 0.000 | 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 | 0.001 |
| 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".