Bibliographic record
Abstract
The author of this paper has been directly involved in the evolution of the maintenance of the New Zealand road network as it moved from the “one stop shop” approach of the New Zealand Ministry of Works in the early 1980’s, through to its’ current position, which is based on complete funder/provider separation, with all services being provided by a fully contestable market. The author has also had the opportunity to observe developing practices in a number of other countries including Australia, the United Kingdom and Canada. As Road Controlling Agencies gain confidence in the success of outsourced maintenance, the scope of the work being outsourced tends to be extended to encompass the full range of asset management activities. This has evolved to the point where contracts that entail the long term management of a road network have been let in a number of countries. The paper draws on the author’s experiences to: (1) define the various roles in the management of a road network and how the procurement models impact on the road controlling authority’s residual roles and responsibilities; (2) discuss the evolution of maintenance contracts as they have moved from initially being essentially “input” based, then to “output” based and now, increasingly, “performance” based contracts; (3) outline the predominant models now being used in New Zealand and the author’s thoughts on their applicability; and (4) illustrate some of the benefits contracting out has delivered and discuss some of the difficulties encountered along the way.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".