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Record W2157663371 · doi:10.1068/b37161

Investigating Travel Thresholds for Sports and Recreation Activities

2013· article· en· W2157663371 on OpenAlexaffabout
Jamie Spinney, Hugh Millward

Bibliographic record

VenueEnvironment and Planning B Planning and Design · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsRecreationDuration (music)Nova scotiaOrder (exchange)GeographyTravel behaviorApplied psychologyAdvertisingMarketingTransport engineeringPsychologyBusinessPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Central to the practice of urban planning is the provision of services, which has potential public health and social welfare implications. However, service area analysis, typically, employs arbitrary travel-distance thresholds. Through this study we provide an empirical investigation into the durations and distances that respondents are willing to travel in order to engage in various sports and recreation activities. This research uses time-diary data, augmented with global positioning system information, to investigate individually based and objectively measured travel thresholds (which define travelsheds) for various sports and recreation activities in Halifax, Nova Scotia. Results accord with national time-diary data, and indicate that duration-based and distance-based travelsheds are generally in the order of 15–30 minutes and 4–20 km, respectively. Georeferenced time-diary data provide unique insights into travel thresholds, which may be used to help improve accessibility and thereby increase the frequency and duration of physical activity engagement.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.138
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.279
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2013
Admission routes2
Has abstractyes

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