Faktor Penyebab Contract Change Order pada Proyek Konstruksi Sumber Daya Air Padang Pariaman
Bibliographic record
Abstract
Government infrastructure projects in water resources projects generally apply a unit price contract system. This is because implementing a unit price contract system is not difficult and is balanced in terms of sharing risks from contract changes between providers and users of construction services. In the unit price contract system, it is very possible for contract changes to occur on construction projects. Changes in the implementation of construction projects can occur repeatedly and are difficult to avoid. The aim of the research is to identify the factors that cause Contract Change Orders and the dominant factors that cause them during the implementation phase of construction projects in the water resources sector. The research was carried out by distributing questionnaires to respondents involved in water resources construction project work at the Padang Pariaman PUPR Service for the 2019 to 2021 budget year and then carrying out factor analysis. In research on the factors that cause Contract Change Orders to occur during the implementation phase of water resources construction projects in Padang Pariaman Regency, there are 4 factors that cause Contract Change Orders during the implementation phase of water resources construction projects, namely managerial factors, regulatory factors. from parties who have the authority to make decisions, planning factors, design change factors. The dominant factor that causes the Contract Change Order to occur during the implementation stage of a water resources construction project is the managerial factor.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.112 | 0.035 |
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".