The Impact of Road Infrastructure in Pengkadan Baru Village on Students' School Interest in Sma Negeri 1 Kelam Permai
Bibliographic record
Abstract
Students' willingness to attend school is greatly affected by the state of the infrastructure, especially the condition of the roads they travel on. Roads play a crucial role in making their journeys to school possible. Ideally, road infrastructure should be both safe and functional. However, at SMA Negeri 1 Kelam Permai, 26 students from the village of Pengkadan Baru face a daily challenge, they must travel on roads that are severely damaged to reach their school. This challenging situation led the researcher to explore how the poor road conditions in Pengkadan Baru affect students’ motivation to attend school. Through quantitative descriptive analysis and involving 26 students from Pengkadan Baru Village as respondents to support this research. The findings show that the road infrastructure in Pengkadan Baru is classified as "Severely Damaged" by the PUPR Department, with a rating of 68.27%. At the same time, students’ motivation to attend SMA Negeri 1 Kelam Permai is categorized as "Very Low," with a score of 78.50%. A Spearman test revealed a significance level of 0.039 (<0.05), indicating a statistically significant relationship between the road conditions and students’ motivation. In conclusion, the severely damaged road infrastructure in Pengkadan Baru has a noticeable impact on the students' desire and ability to attend school regularly at SMA Negeri 1 Kelam Permai.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".