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Record W588671844

Sustainable Transportation: Review of Proposals, Policies, and Programs 2000-2007

2009· article· en· W588671844 on OpenAlexaboutno aff
Jiangping Zhou

Bibliographic record

VenueTransportation Research Board 88th Annual MeetingTransportation Research Board · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsMandateSustainabilitySustainable transportGovernment (linguistics)BusinessEuropean unionGateway (web page)Sustainable developmentSustainable communityPublic administrationTransportation planningPolitical scienceTransport engineeringEngineeringEconomic policy
DOInot available

Abstract

fetched live from OpenAlex

This paper reviews the most recent research, policy proposals and recommendations, implemented policies, and programs on sustainable transportation at the national (federal) level, with regional focus on the US, UK (related to European Union if appropriate), and Canada. The paper found that the concept of sustainable transportation had been given increased attention recently. However, there are significant variances between the research, policy proposal, and implementation. Efforts made towards sustainable transportation, and the focus of the efforts within and outside the US also vary notably. As a whole, the US federal government was less aggressive than its British and Canadian counterparts in marketing and implementing sustainable transportation. This is evidenced by a lack of overarching federal policy (mandate) or even a working definition for sustainable transportation, absence of an organized approach (such as gateway and dedicated websites) within governmental agencies to market the idea of sustainable development in general and sustainable transportation in particular, and discrete rather than coordinated plans on sustainability and sustainable transportation issues.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0020.003
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.412
Teacher spread0.358 · 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 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

Citations4
Published2009
Admission routes1
Has abstractyes

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