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Record W2166487586 · doi:10.36368/jns.v4i1.632

Regional Development, Transport Infrastructure and Government Policy

2010· article· en· W2166487586 on OpenAlexaboutno aff
Martin Eriksson

Bibliographic record

VenueJournal of Northern Studies · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsTransport infrastructureGovernment (linguistics)BusinessCritical infrastructureRegional scienceRegional developmentEnvironmental planningPublic administrationPolitical scienceGeographyTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Due to the cold climate, navigation along the coast lines of the northern regions in Sweden, Finland, Canada, Russia and the United States must negotiate winter conditions which cause ports to freeze over. In order to avoid the negative economic effects of such interruptions, ice-breaking and other measures to facilitate winter navigation have been introduced. This article deals with the introduction of ice-breaking along the coast line of the five northernmost counties in Sweden, the Norrland region, from a perspective that examines and analyzes the underlying decision-making processes. It is concluded that the ability of regional interest groups to link their demands for an improved ice-breaker service to important aims within macro policy such as trade policy, growth policy and regional development policy contributed to the outcome of the decision-making processes. The international competitiveness of the export industries in Norrland was therefore regarded as a national concern during the decision-making processes. Another factor that contributed to the outcome of the decision-making processes was the sectoral organization within the government maritime bodies. Large-scale planning and operational experimentation was allowed to take place within the ice-breaker service, which convinced the government that ice-breaking and winter navigation were a feasible transport alternative.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.429

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 head0.025
GPT teacher head0.232
Teacher spread0.208 · 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 designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

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