Owner and Contractor Choices and Dilemmas: Contemporary Project Delivery Options
Bibliographic record
Abstract
Governmental agencies have increasing options for project delivery and contractual arrangements when procuring and constructing public infrastructure projects. Contractors face the dilemma of selecting the right projects in which to invest their limited pre-construction financial and staff resources. This research investigates the project delivery and procurement methods selected for three large public bridges: Third Crossing Bridge in Kingston, Ontario, the Highway 53 relocation bridge located near Virginia, Minnesota, and the Warman and Martensville interchanges bridges in Saskatoon, Saskatchewan. Data collected through structured interviews with key stakeholders from the respective public agencies and Kiewit Corporation, the common contractor for the projects, compares tri-party/integrated form of agreement (IPD), construction manager/general contractor (CM/GC), and design-build (DB) project award processes and decision-making from the perspectives of the governmental agency and the general contractor. The research results in a comparative diagram summarizing the key advantages and challenges of these project delivery options for use by owners and contractors in their procurement and decision-making processes.
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 | 0.100 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".