Risk Analysis of Tender Documents on the Execution of Private Construction Work at Badung Regency, Bali Province, Indonesia
Bibliographic record
Abstract
Documents received during the tender, are in the form of drawings, specifications, bill of quantity (BQ), and the general terms of the contract. Tender activity will bring a variety of risks during the project implementation, and this research is done to identify and assessment of risks, mitigating risk and determining the ownership of the dominant risk.The research was conducted on a private building construction project at Badung Regency in Bali, by using a qualitative method and data collection was done through interview, brainstorming with experts and questionnaires. Among the 39 risk identified, 15 risks were obtained from the previous research, and the remaining 24 risks in this research.The results of this risk assessment were that 18 risks (46.2%) categorized into unacceptable, that include: the addition items of work, the drawing does not match with plan, and the changes in the material specifications. Risk assessment fell into the undesirable category that 21 risks (53.8%), including the mismatch information from planners, the arithmetic error, and materials used were not available on the market. Mitigation was done to dominant risk, among others by reassessing, submitting the contract change order, and asking questions. The biggest risk of ownership was the contractor, namely 39 risks with 18 unacceptable and 21 undesirable risks, this mean, problems associated with tender documents should receive the attention to contractors, planner consultants, owners and Quantity Surveyor (QS) consultants. Contractors as the recipient of the biggest risk were expected to increase the competence of those involved in the tender process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".