Analysis of the effects of capital expenditure and supply chain on economic growth and their implications on the community welfare of districts and cities in central Kalimantan province
Bibliographic record
Abstract
This study aims to examine the relationships between capital expenditure, supply chain activities, economic growth, and community welfare in the districts and cities of Central Kalimantan Province. The study analyzes the original sample data from 2014 to 2022 using a path analysis approach. The findings reveal significant positive relationships between capital expenditure, economic growth, and community welfare. However, the hypothesized positive relationships between supply chain activities and economic growth, supply chain activities and community welfare, and economic growth and community welfare are not supported. Moreover, the mediating roles of economic growth in the relationships between capital expenditure and community welfare and supply chain activities and community welfare are not confirmed. Theoretical implications highlight the importance of strategic capital expenditure in driving economic growth and enhancing community welfare. Practically, the study guides policymakers and development planners to allocate resources effectively for sustainable and balanced economic expansion. The study has limitations, including focusing on direct relationships and the specificity of Central Kalimantan Province. Future research could include external factors, apply cross-regional analyses, and utilize qualitative approaches to gain deeper insights. Additionally, spatial analyses could enhance understanding of geographic impacts. The novelty of this research lies in its comprehensive exploration of the interplay between capital expenditure, supply chain activities, economic growth, and community welfare in the context of Central Kalimantan Province. By shedding light on the intricate dynamics between these variables, this study contributes to the existing literature on regional development and public policy while offering insights for informed decision-making and sustainable development practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".