Bibliographic record
Abstract
In Australia and New Zealand, there has been a movement toward the private-sector delivery of road maintenance and management by using the performance-specified contract (PSMC) model. These are long-term contracts tendered competitively with a lump sum price. Initially the contracts concentrated on the physical attributes of the network that had to be maintained for the contract period. However, as these contracts matured, a reduction in the crash rates was observed. It was considered that the successful operation of a PSMC contributed to this reduction. By using data and calculation methods developed by Land Transport New Zealand and applying social costs for crashes normally used for justifying capital projects, it can be seen that the social cost of crashes is being reduced at a significantly greater rate on the PSMC 001 network than on the remainder of the state highway network. The value of savings ahead of the national trend has been more than NZ$31 million for a 3-year period. The contractor's performance is measured on the social cost of crashes that occur on the network, regardless of crash causation. To keep the contractor motivated, the contract includes provisions to adjust the contract payments as based on the safety performance. This approach requires a fundamental shift in the attitude of the contractor, moving from a reactive position to a new position of prevention with particular attention on improved safety. Performance data from a 7-year contract and a 3-year contract from New Zealand are presented. Improved safety performance has become a hallmark in these contracts, and safety performance continues to improve through innovations and commitment.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".