Risk management and risk management performance measurement in the construction projects of Finland
Bibliographic record
Abstract
Distinguishing and diminishing risks in today's projects are crucial for project success.Almost every project is facing several risks throughout the project timeline.Construction projects in Finland are also facing project risks due to the complexity of the project.To minimize the impact of risks, an effective risk management approach must be incorporated into every project which also includes the effectiveness and measurement of its performance.Managing the risks is an important job but measuring the RM performance is crucial.Thus, the objective of this study is to analyze the risk management (RM) and risk management performance measurement (RMPM) through an in-depth empirical analysis of two complex construction projects of Finland.To achieve the objective, a qualitative case study is followed by the authors of this article to identify the RM processes, major and minor risks of the projects, RM strategies to mitigate them and RM performance measurement strategies.Overall, this article provides a comparative analysis of RM and RMPM for construction projects and it can be used as a basis for further research into RM perspective in complex construction projects.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".