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Privatising Railroads: The Problematic Involvement of the Private Sector in Two Dutch Railway Projects

2005· article· en· W1991039965 on OpenAlexaff
Joop Koppenjan, Martijn Leijten

Bibliographic record

VenueAsia Pacific Journal of Public Administration · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsEurosActivity-based costingGerman governmentGovernment (linguistics)GermanBusinessPrivate sectorLine (geometry)Transport engineeringRegional scienceEngineeringEconomyEconomicsEconomic growthGeographyMarketing

Abstract

fetched live from OpenAlex

This article provides a comparative analysis of the Dutch government’s attempts to involve private parties in two national railroad mega-projects: the Betuwe Line (a transit line for goods connecting the Rotterdam harbour to the German rail network — costing 4.8 billion euros) and the HSL-Zuid (a high speed line between the Belgian border and Amsterdam — costing 6.8 billion euros). These projects are currently under construction. To date, the government has not succeeded in privatising the Betuwe Line. Although the privatisation of the HSL-Zuid has been realised, it has proven difficult to keep under control. The two projects are compared in terms of the motives for privatisation, the strategies adopted, and the results achieved. Explanations are sought for the extent to which the strategies were successful. Generic lessons are drawn from the two projects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.006
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.248
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2005
Admission routes1
Has abstractyes

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