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Record W6958316224 · doi:10.6084/m9.figshare.14988385

Comparing Household and Individual Measures of Access through a Food Environment Lens: What Household Food Opportunities Are Missed When Measuring Access to Food Retail at the Individual Level?

2021· article· en· W6958316224 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical accessHealthy foodHousehold incomeFood insecurityPublic accessData collection

Abstract

fetched live from OpenAlex

Geographers and public health researchers have long been interested in social, spatial, and economic factors that drive access and exposure to food retail. A growing body of evidence draws on mobility data to capture locations accessed by individuals beyond the home address. Given that food-related activities are shared by household members and often gendered, taking an individual-level approach might limit researchers’ ability to accurately describe access to food retail. Using data that includes Global Positioning System trajectories of forty-six adults from twenty-one households in Toronto, this study compares access to food retailers at the individual and household levels and evaluates measurement issues that arise when relying on one household member. Spatial and spatiotemporal measures of access were derived from individual and total household activity spaces. Differences in access were tested for men and women and moderating effects of neighborhood, shopping responsibility, car access, and employment status were investigated. Supermarket density was greater for women when compared with men in the household, as well as their total household measure. Additionally, within-household differences in counts of supermarkets were moderated by neighborhood. Future research should consider the role of place and the contributions of household members when measuring access to food at the individual level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.890
GPT teacher head0.257
Teacher spread0.633 · 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.

Study designNot applicable
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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