The Effect of Village Income on Village Expenditure: A Case Study of Belitung Regency
Bibliographic record
Abstract
The development progress of rural areas generally lags behind that of urban areas.To bridge this gap, the support of various factors is essential, with financing being one of the key elements.In rural regions, several financing sources can be utilized to bolster development efforts, such as Village Original Income (PAD), Transfer Funds, and other financial resources.Belitung Regency, as an archipelagic area within the Bangka Belitung Islands Province, undoubtedly requires financial support to foster developmental progress within its jurisdiction, given the pivotal role of financing in development.To comprehend the impact of financing, particularly on village expenditures in rural locales, it is imperative to undertake research.Hence, this study is designed to examine and analyze the influence of village income on village expenditures in Belitung Regency.The Geographically Weighted Regression (GWR) is one analytical model applicable for assessing the impact of village income on village expenditure.Data for this study is amassed through observation, with some obtained from specific agencies.Utilizing the GWR analytical model will elucidate the varying influences of village income on village expenditure across individual villages, since the GWR method is an advanced form of simple regression analysis that incorporates spatial elements to yield more granular, regionspecific outcomes.The findings from the GWR analysis indicate that village funds and allocations have a positive effect on village expenditures in certain areas, signifying that they can increase spending.However, in other regions, these same financial instruments display a negative impact due to poor planning.Additionally, variables such as profit sharing, bank interest, aid grants, and general village original income positively influence village spending, suggesting that an increase in these variables can bolster spending in Belitung Regency.It is recommended that stakeholders engage in meticulous financial planning to maximize the potential of village funds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".