Bibliographic record
Abstract
This study aims to Analyzing the nature of Construction Projects, Analyzing the Nature of Construction Projects Risk, and Analyzing The mechanism of Risk Management. This Study adopted The Quantitative Method. The Summary Concluded From The Study Lies in the Theoretical Study of construction Project`s Risk. The Construction Project Contains Many Risk Which Related to Different Factors: Legal, Organizational, Technical, Zoning, Financial, Social and Political Factors. The Process of Management of Construction Projects includes: Planning of Risk Management, Risk Identification By (Checklist Analysis, Questionnaire, Personal Interview, Brainstorming Technique, Delphi Technique), Risk Analysis By Qualitative Analysis By (Probability and Impact Assessment, Cause and Effect Diagram, Probability and Impact Matrix) and Quantitative Analysis By (Probability Distributions, interviews , Sensitivity Analysis, Fault tree, Events tree, Munte Carlo Simulation), Planning the Response to Risk By (Strategies for Response to Negative Risk , and Strategies to Positive Risks), and Risk Control and Cheek. Depending on The Conclusions, The Study Recommends the Following: Process of Assessing The Efficiency of Construction Companies. Use Qualitative Analysis and Quantitative Analysis in The Process of diagnosis, and Categorization of Risk in the Process of Risk Management. Studying Types of Contracts of Construction Projects.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".