Assessing the Socio-Economic Impact of Infrastructure Development on Local Communities: A Mixed-Methods Approach
Bibliographic record
Abstract
This study analyzes the impact of infrastructure development on the socio-economic aspects of local communities using a mixed approach that combines quantitative and qualitative analysis. The aim of the research was to explore the relationship between infrastructure development and its effect on the welfare of the local population. The main findings show that the impact of infrastructure development has various and complex dimensions. The results of the analysis show that the impact is multidimensional, covering economic aspects such as growth and new job opportunities, as well as social aspects such as changes in lifestyle and social interaction. This research also highlights that the impact of infrastructure can vary significantly between different regions, depending on the type of infrastructure built and local characteristics. In this context, accessibility is found to be a factor that strongly influences socio-economic impacts. Infrastructure that increases accessibility can contribute to economic growth and increase people's welfare. In addition, community participation in the planning and implementation stages of infrastructure projects has also proven to have an important role in influencing the impact felt
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".