‘Beyond policy tourism’: the international lived experience of cycling in the Netherlands and Canada
Bibliographic record
Abstract
As more cities implement cycling infrastructure, there is a growing need to both learn best practices from other places through a detailed understanding of the lived and embodied experiences of cycling. However, this is rarely the case. On the one hand, planners and policymakers rely on incomplete (quantitative) data and policy tours that are unable to document the full extent of cycling or how it is experienced. While recent studies have expanded to include qualitative methods, they are predominantly conducted in one place, limiting our ability to draw international comparisons. The Netherlands is a popular destination for such tours and is generally regarded as one of the best places in the world for cycling. But what about people who, for a variety of reasons, have lived in different countries? Their knowledge, experiences and reflections on cycling are rarely featured in planning. To redress this, we interviewed participants who have international experience, capturing beyond aspects of policy tourism, illuminating how: (1) mixed land-use patterns, (2) incentivizing cycling as a mode choice, and (3) cycle networks and safety are vital to cycling participation. We advance the cycling research agenda by examining these findings and proposing changes best suited to low-cycling cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".