A multi-perspective assessment method for measuring leading indicatiors in capital project benchmarking
Bibliographic record
Abstract
This paper presents a new multi-perspective assessment method for measuring leading indicators deployed in the 10-10 Performance Assessment System that the Construction Industry Institute (CII) has recently launched. The CII 10-10 Performance Assessment System adopted a multi-perspective assessment approach for evaluating leading indicators that represent various management input measures throughout capital project delivery process. The leading indicators consist of 10 input measures, including four fundamental management functions such as planning, organizing, leading, and controlling as well as major management practices such as design efficiency, human resources, quality, sustainability, supply chain, and safety. This paper provides the theoretical background for the method through extensive review of existing benchmarking theories. Then it describes the development process for the assessment method. After this, it presents how the method was deployed to evaluate the system’s 10 leading indicators. Finally, this paper discusses how to practically utilize the input measure scores acquired from the method for performance improvement. The assessment method in the system will help project management teams to diagnose their project’s performances and thus allow them to set up proactive strategies for the subsequent phases of the project.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".