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Record W7024659975

The Role of Neighbourhood Landscape Characteristics in Facilitating Outdoor Play During the COVID-19 Outbreak

2021· dissertation· en· W7024659975 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Outdoor activityPopulationOutbreakUrban landscapePhysical activityPopulation density
DOInot available

Abstract

fetched live from OpenAlex

Children’s outdoor activities were significantly impacted by the COVID-19 outbreak in Canada. Children became less active as access to outdoor places where they typically play and exercise was restricted or denied. This research investigated how changes in outdoor activity of 5-17 year-olds are associated with neighbourhood landscape characteristics. This study uses secondary data on changes in outdoor activities fromParticipACTION and analyzes these changes in relation to four landscape characteristics, one demographic measure, and common places where children spent time outdoors. Results show that living in areas with more landscape features and higher population density is associated with a greater decline in outdoor activities, and children who used neighbourhood trails were more likely to show an increase in outdoor activities during the first wave of COVID-19 in Toronto. This indicates that landscape characteristics support children’s physical activity when playgrounds are closed.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.704
Threshold uncertainty score0.595

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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