The effects of information and communication technology on village development performance
Bibliographic record
Abstract
The purpose of this study is to examine how Information and Communication Technology (ICT) affects village development performance, which is measured through the Developing Village Index (VDP). This index is made up of three dimensions: Social, Economic, and Environmental Resilience Indexes. By using quantitative methods and cross-sectional data from 1,842 villages in the Central Sulawesi Province in 2021, the study concludes that ICT has a positive impact on the development performance of villages overall. The study found that the quality of internet signals and the number of cell phone users, as well as the presence of technology devices and internet facilities in village offices, have a positive influence on village development performance. However, the existence of internet facilities for the public has no effect on village development performance, including economic, social, and environmental development in rural areas. By examining the effectiveness of both the community and the village government's utilization of ICT, this study contributes to understanding the impact of ICT in improving village development performance. To reduce the digital divide and continue to support rural development, it is important to enhance ICT facilities and infrastructure for rural communities and improve the quality of their implementation governance. Furthermore, to support the delivery of public services and the implementation of village development programs, both the government and Regional Government need to encourage the effectiveness of the use of ICT.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".