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Record W3134541424 · doi:10.3390/su13063056

Home Food Gardening in Canada in Response to the COVID-19 Pandemic

2021· article· en· W3134541424 on OpenAlexafffundabout
Lisa Mullins, Sylvain Charlebois, Erica Finch, Janet Music

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Exploratory researchProvisioningResilience (materials science)2019-20 coronavirus outbreakPsychological resilienceEnvironmental healthGeographyBusinessPsychologyMedicineSociologyEngineeringSocial science

Abstract

fetched live from OpenAlex

The lack of academic attention that home food gardening has received in Canada and the United States is surprising, given the many demonstrated benefits of community gardening programs, including increased community cohesion and resilience. The aim of the exploratory study is to explore the current surge in home food gardening and its relationship to the COVID-19 pandemic. A national survey was conducted, consisting of 43 main questions, asking respondents about their home life and food provisioning during COVID-19, the physical characteristics of their food gardens, and their attitudes and beliefs concerning home food production. Survey results show that 51% of respondents grow at least one type of fruit or vegetable in a home garden. Of those, 17.4% started growing food at home in 2020 during COVID-19 pandemic. To gain more insight into just how significant a cause the pandemic lockdown was on home food gardening, follow-up surveys and policy recommendations are suggested.

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)0.002
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 score0.051
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.236
Teacher spread0.216 · 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

Citations100
Published2021
Admission routes3
Has abstractyes

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