Early Contractor Involvement and Target Pricing in U.S. and UK Highways
Bibliographic record
Abstract
A U.S. panel of federal, state, and private-sector professionals traveled to Canada and Europe in May 2004 to conduct a scan of construction management practices for effective project delivery, contract compliance, and quality assurance. The purpose of this research was to review and document international policies, practices, and technologies for potential application in the United States. The research team observed construction management methods that promote alignment of team goals, integrated use of risk analysis techniques, strategic use of alternative delivery methods, procurement systems that set a framework for success, contract payment methods that support alignment and trust, a delegation of traditional highway agency functions to promote efficiency, a philosophy of network management, and a greater partnership between public and private agencies. One of the significant discoveries dealt with the use of target pricing by the Highways Agency in England on its early contractor involvement projects. As a result of this scan finding, a target pricing proposal was submitted to the South Carolina Department of Transportation (DOT). Additionally, it was discovered after the scan that a form of target pricing has been employed in the state of Washington. Target pricing concepts from the Highways Agency in England, South Carolina DOT, and Washington State DOT are summarized. The aim of the target pricing technique is to align team goals by establishing the contractor's role early in the project development process and then sharing risks rationally and equitably through to project completion.
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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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".