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Record W2586486509

Effektiva kontraktsmodeller för vägunderhåll

2016· article· sv· W2586486509 on OpenAlexaboutno aff
Johannes Österström, Jan-Eric Nilsson

Bibliographic record

VenueVTI Rapport · 2016
Typearticle
Languagesv
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveQuality (philosophy)Competition (biology)BusinessReimbursementActuarial scienceBalance (ability)Operations managementEconomicsMicroeconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

The study in this report reviews international experiences of contracting of routine road maintenance. The study describes the contracting models used in Norway, Scotland and Ontario (Canada), as well as the results from the relevant research literature. Focus has been to map differences in efficiency between traditional and performance-based contracting, as well as the effects of incentives, risk sharing, competition and quality dimensions in contracting. The results show that Ontario and Norway have largely performance-based contracting models. Recently, some aspects of these contracts have been reconsidered because of problems mainly with respect to winter maintenance. In Ontario, quality is now considered as one component for identifying the winning bidder; Norway has a new reimbursement model that aim to balance the contractors' incentives. Also, Norway is testing new contracting models in five areas. Scotland uses traditional contracts and is testing a new performance indicator to improve winter maintenance by using measurements of friction. In the literature, the view on performance-based contracting is often positive. But no study has been found that measures the efficiency of different contracting models that accounts for possible simultaneous changes in quality or long-term effects.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0270.013

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 head0.109
GPT teacher head0.330
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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