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Record W2128525207 · doi:10.1123/jpah.9.2.153

Walking for Transport Versus Recreation: A Comparison of Participants, Timing, and Locations

2012· article· en· W2128525207 on OpenAlexaffabout
Jamie Spinney, Hugh Millward, Darren M. Scott

Bibliographic record

VenueJournal of Physical Activity and Health · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsRecreationMcNemar's testPsychologyPhysical medicine and rehabilitationDemographyPhysical therapyMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Walking is the most common physical activity for adults with important implications for urban planning and public health. Recreational walking has received considerably more attention than walking for transport, and differences between them remain poorly understood. METHODS: Using time-use data collected from 1971 randomly-chosen adults in Halifax, Canada, we identified walking for transport and walking for recreation events, and then computed participation rates, occurrences, mean event durations, and total daily durations in order to examine the participants and timing, while the locations were examined using origin-destination matrices. We compared differences using McNemar's test for participation rates, Wilcoxon test for occurrences and durations, and Chi-Square test for locations. RESULTS: Results illustrate many significant differences between the 2 types of walking, related to participants, timing, and locations. For example, results indicate a daily average of 3.1 walking for transport events, each lasting 8 minutes on average, compared with 1.4 recreational walking events lasting 39 minutes on average. Results also indicate more than two-thirds of recreational walks are home-based, compared with less than one-fifth of transport walks. CONCLUSIONS: This research highlights the importance of both types of walking, while also casting suspicion on the traditional home-based paradigm used to measure "walkability."

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)0.001
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 score0.191
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.252
GPT teacher head0.481
Teacher spread0.229 · 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

Citations68
Published2012
Admission routes2
Has abstractyes

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