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Record W610494263

Incentives, Results and Possible Success Factors for Rail Maintenance Performance-Based Contracting : Case Study

2010· article· en· W610494263 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueConstruction Management and Economics · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveBiddingPaymentPrestigeBusinessIndustrial organizationEconomicsFinanceMarketingMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Knowledge of what action that is needed to drive innovation at a desired speed is in demand in civil engineering and its related maintenance. 1. What measures to stimulate innovation have been tested? 2. How much innovation has been achieved by contracting? 3. How much innovation was achieved by performance-based specifications? 4. How can cost models contribute to innovation? Methods include qualitative and quantitative methods that have been timed and mixed to optimize their merits. Sweden, France, USA and Canada have used as research ground. Technology transfer, multi-criteria evaluation, variant bidding, idea mailbox, weatherregulated payment, contests and earmarked funds for innovative projects were some of the method beside and within contracting and performance-based specifications that have been tested. Contracting as such has cut costs in Sweden but not in North America. Neither Sweden nor North America has noticed any increase of innovation, rather the contrary. The savings have primarily been achieved by cuts on staff and by using standardized, less expensive and less advanced machinery. Contracted highway maintenance provinces in Canada and Sweden on average had about 50 % higher costs than inhouse provinces and Washington State. The difference is reduced to 26 %, when corrected by weather and the higher traffic in the contracted provinces. Prestige, politics and competitivity made it difficult to extract economic data from private contractors, and even from the public owners and may explain the contradictory results in previous studies. The internally driven innovation appears small and incentives to innovation weak in inhouse systems, but contrary to expectation even less in contracted systems. Performance-based specifications (PBS), such as Design-Build (DB), have reduced delivery times and kept the budget better than traditional contracts, but quality, lifecycle cost and technical progress was rarely analyzed and even less confirmed in the literature, why a multiple case study was carried out. The result was that three out of four PBS cases delivered lower quality in the long run or showed higher costs already on the opening day, when compared to a traditional contract alternative. Cost models contribute to innovation by making regions with different conditions comparable and provide tools for rational planning and decision making. One model for how highway maintenance costs depend on snow, bridges and traffic and one model for how bridge maintenance costs depend on size and age were elaborated. Models included in contracts, e.g. to allow a contractor to reduce the weather risk, appear to have contributed to a more successful contracting rollout in Sweden than in Canada. France provides experience of how inhouse innovation contests and industry-own patent-like routines can promote innovation. After the first two years with an incentive contract, Banverket received 10 % better quality measured as train delay and 20 % better quality measured as the number of technical errors at no cost. A lesson learnt is that the success of performance-based specifications depends on how well the owner can describe and define the contracts, how compliance is measured and how deviations are handled, i.e. how the contractor is penalized for non-fulfillment or awarded for excess delivery

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.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.058
GPT teacher head0.312
Teacher spread0.254 · 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