Good Practice in the Exploitation of Innovative Strategies in Sustainable Urban Transport: City Interview Synthesis
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".