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Record W4388021153 · doi:10.1080/21650020.2023.2276406

‘Beyond policy tourism’: the international lived experience of cycling in the Netherlands and Canada

2023· article· en· W4388021153 on OpenAlexafffundabout
Rebecca Mayers, Brian Doucet

Bibliographic record

VenueUrban Planning and Transport Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Waterloo
FundersCanada Research ChairsCommunity Foundation
KeywordsCyclingRedressVariety (cybernetics)TourismLimitingLived experiencePublic relationsPolitical scienceGeographyPsychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

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.

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)0.000
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 score0.184
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.091
GPT teacher head0.407
Teacher spread0.315 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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