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Record W1537161629

Good Practice in the Exploitation of Innovative Strategies in Sustainable Urban Transport: City Interview Synthesis

2009· article· en· W1537161629 on OpenAlexaboutno aff
Greg Marsden, Karen Trapenberg Frick, Anthony May, Elizabeth Deakin

Bibliographic record

VenueeScholarship (California Digital Library) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCommitContext (archaeology)Policy transferProcess (computing)Knowledge transferPublic relationsPoliticsPolitical scienceBusinessSociologyPublic administrationRegional scienceKnowledge managementGeographyComputer science
DOInot available

Abstract

fetched live from OpenAlex

A literature review of policy transfer in transport and cognate fields was conducted. It shows that there is little evidence on how cities learn from each other and even less on how this process occurs in the transport sector.The review identified a series of key aspects of policy transfer which the literature suggests might be important in understanding the process of, advantages and barriers to transferring innovative transport policies.Interviews were then conducted in 11 cities to further investigate the process of policy transfer and the role of academics within this. Seven cities were studied in Northern Europe (Leeds, Edinburgh, Stockholm, Copenhagen, Bremen and Lyon, Nancy) and four in North America (Vancouver, Dallas, San Francisco and Seattle). This report presents the results of the synthesis of the city interviews.The key findings are:1. Cities are actively looking to learn from another but this process is unsystematic and sometimes inefficient2. The search for new policies is constrained by a lack of resources, particularly personnel3. Informal networks and information sharing based on professional contacts is the predominant method of initial knowledge transfer4. Local context is critical in determining whether policies will transfer well across cities and lack of fit is one reason for limited transfer5. Institutional barriers also exist to policy transfer which seem most likely to influence what gets implemented rather than what gets considered6. Key facilitators to overcome barriers to implementation are:a. A supportive political environment;b. Sufficient staff resources to commit to the projects;c. A culture of engaging with other cities and a structure that allows for staff at all levels to seek out information by contacting staff internally or at other organizations that are of a different staff/management level;d. An internal organisational culture to try new things; ande. co-funding of implementation from other government tiers or the private sector7. Academic research is one potential source of information on innovation and implementation but one which is underutilised in many cities. This was particularly true of the European cities compared with those in North America8. The academic and practitioner networks are not well connected and there are both practical and cultural barriers to better integration. In the light of these findings and a more detailed consideration of the cultural and practical barriers to better integration between academics and practitioners nine potential areas for future action are identified.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.010
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.291
Teacher spread0.263 · 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 designTheoretical or conceptual
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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