Bibliographic record
Abstract
Performance-based contractor prequalification goes beyond the financial prequalification provided by the surety industry when it issues a bond for a public transportation project to include a contractor's performance record in the prequalification process. This paper reports the results of a survey of the status of performance-based contractor prequalification from 41 U.S. state departments of transportation (DOTs) and seven Canadian provincial ministries of transportation. These results were correlated with a content analysis of 43 DOT administrative prequalification documents and 62 sets of project-specific, performance-based prequalification documents. The findings were validated through structured interviews with contractors. The study found that performance-based contractor prequalification can be portrayed as a three-tiered system. The first tier mirrors the current administrative prequalification systems. The second tier is performance-based and includes postproject contractor evaluations, and the final tier consists of project-specific prequalification. This system constitutes a framework from which a transportation agency can design a contractor prequalification system that directly rewards good performers and encourages poor performers to improve. These features of the system are accomplished by adjusting, on the basis of a given contractor's past performance, its bidding capacity and the amount of performance bond that it is required to provide.
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 | 0.090 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".