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Record W1979426043 · doi:10.1080/21650020.2014.906910

The impact of planning policies on bicycle-transit integration in Calgary

2014· article· en· W1979426043 on OpenAlexaffabout
Sasha Tsenkova, David Mahalek

Bibliographic record

VenueUrban Planning and Transport Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCyclingTransport engineeringPublic transportTransportation planningTransit (satellite)BusinessEnvironmental planningGeographyEngineering

Abstract

fetched live from OpenAlex

Efforts to manage Canadian cities through smart growth policies emphasize the importance of integrated public transit system and bicycle-transit integration. The goal of this paper is to review the impact of planning policies that promote utilitarian cycling on the provision of bicycle facilities (pathways, bikeways and parking) in Calgary. The focus is on new suburban communities built since the 1990s, where new policies and standards affecting cycling have been implemented. The methodology draws on literature review, content analysis of major planning policies affecting utilitarian cycling, GIS spatial analysis of three case study areas and key informant interviews to holistically assess levels of bicycle-transit integration in Calgary. The research applies a straightforward and relatively robust framework for analysis of bicycle-transit integration using a number of quantitative indicators to assess levels of provision/accessibility and connectivity in transit commuter zones. The spatial analysis confirms that newer communities have better developed commuter-oriented cycling networks, bicycle facilities and integration with the light transit system compared to older ones. Despite area-specific challenges, findings indicate that the shift in planning policies in Calgary has a positive impact over the level of provision of bicycle infrastructure, which have the potential to increase utilitarian cycling in the future.

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)0.003
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 score0.024
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.429
Teacher spread0.351 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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