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Record W1584241615 · doi:10.1007/bf03403732

Measuring and mapping disparities in access to fresh fruits and vegetables in Montréal.

2008· article· en· W1584241615 on OpenAlexaboutno aff
Lise Bertrand, François Thérien, Marie‐Soleil Cloutier

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsGeographySustainabilityPopulationGeoreferenceBusinessAgricultural economicsIndex (typography)Agricultural scienceSocioeconomicsCensusEnvironmental healthMedicineEnvironmental scienceEconomicsEcology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was conducted to evaluate disparities in access to healthy food in Montreal, focusing on the availability of fresh fruits and vegetables (F/V) as an indicator. METHOD: F/V selling area was measured in all food retail stores and public markets offering more than 75 square feet of fresh fruits and vegetables. An accessibility index was elaborated, taking into account motorization rates and the total surface of these fresh foods for sale within an easily accessible zone. The extent of that zone was determined differently for motorized (3 km) and non-motorized (500 m) consumers. Measures were calculated and georeferenced at the level of "Dissemination Areas" according to the 2001 Census. RESULTS: In general, access to healthy foods is quite good for consumers who shop by car. But 40% of the population have poor access to fruits and vegetables within a walkable distance from home. No relationship is observed between median income in dissemination areas and food supply. CONCLUSION: Improved access to healthy food by non-motorized consumers is needed in many areas of Montreal. Implications of differential access to fresh fruits and vegetables for health and environmental sustainability are discussed.

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 score0.222
Threshold uncertainty score0.964

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 communication0.0000.001
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 head0.090
GPT teacher head0.253
Teacher spread0.162 · 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

Citations49
Published2008
Admission routes1
Has abstractyes

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