Faktor yang Mempengaruhi Rendahnya Kualitas Pekerjaan Konstruksi Jalan di Pasaman Regency
Bibliographic record
Abstract
In an effort to meet the needs for optimum road facilities and infrastructure, through the Public Works and Spatial Planning Department, every year there are road construction and improvement activities in Pasaman Regency. The quality of road construction and improvement projects in Pasaman Regency is relatively low, so researchers conducted an analysis of the things that influence this low quality. The aim of this research is to identify factors that influence the low quality of road improvement and construction projects in Pasaman Regency and analyze the dominant factors. The data collection technique was carried out using a quantitative method, namely distributing questionnaires to respondents. Next, data processing was carried out by testing validity, reliability and factor analysis using SPSS. The results of the research conducted showed that there were 6 factors that influenced the low quality of road improvement and construction projects in Pasaman Regency, namely environmental and technical factors 16.686%, work method factors 12.047%, equipment factors 10.971%, labor factors 10.039%, managerial factors 8.648%, and external factors 7.999%. The dominant factors influencing the low quality of construction work in road improvement and construction in Pasaman district are environmental and technical factors, which consist of the response of the surrounding environment regarding project safety; existing and physical characteristics of buildings around the location; natural disasters; material prices; and funding at the contractor.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".