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Record W571673388

Procurement Models for Road Maintenance

2005· article· en· W571673388 on OpenAlexaboutno aff
Tony Porter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementScope (computer science)Work (physics)Position (finance)BusinessAsset managementAsset (computer security)Road mapOperations managementComputer scienceFinanceMarketingEconomicsEngineeringComputer security
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.214
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2005
Admission routes1
Has abstractyes

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