Development and implementation of a benchmarking and metrics program for construction performance and productivity improvement<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.
Bibliographic record
Abstract
To improve construction productivity and performance, it must be measured. The Construction Sector Council (CSC) has started a Labour Productivity and Project Performance Benchmarking Program for the infrastructure sector of the construction industry in Canada. Metrics were developed for project cost, time, safety, and quality performance; labour productivity; rework; project conditions; and management practices related to health and safety. Data from 19 projects located in different regions of Canada were collected and analyzed. Based on the results and on industry feedback, additional metrics for practices related to project planning, materials management, and construction supervisory skills development were developed. This paper describes the development of the program. Lessons learned during the development and implementation of the benchmarking and metrics program are summarized and steps to establish a sustainable program are identified. It is concluded that a successful program is feasible and has the potential to have broad impact.
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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 | 0.101 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".