MétaCan
Menu
Back to cohort
Record W2198115879 · doi:10.15353/cfs-rcea.v6i1.264

Gleaning in the 21st Century: Urban food recovery and community food security in Ontario, Canada

2019· article· en· W2198115879 on OpenAlexaffvenueabout
Jennifer Marshman, Steffanie Scott

Bibliographic record

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsFood securitySustainabilityBusinessFood systemsEnvironmental planningEnvironmental resource managementPolitical scienceGeographyAgricultureEconomicsEcology

Abstract

fetched live from OpenAlex

Historic gleaning activities in Europe took place in farmers’ fields where gleaners could collect the leftovers of the harvest. One of the primary motivations for modern gleaning in Canadian cities is to donate fresh food to local organizations such as food banks. As there is currently little research in this area, this study aims to explore how gleaning initiatives contribute to community food security. The study is based on interviews and surveys with volunteers from several gleaning organizations in Ontario, combined with the Dietitians of Canada’s Food Security Continuum (FSC) as a framework for analysis. Findings include that gleaning contributes to all three stages of the FCS: initial food systems change, food systems in transition, and food systems redesign for sustainability. Respondents felt that while the amount of food harvested could be scaled up, there were benefits that augmented community food security, such as increased food literacy, food awareness, community cohesiveness, and a fresh food supply. Overall, this study improves our understanding of how gleaning initiatives can contribute to community food security. With better ongoing support from the community and on the policy agenda, such projects could further enhance their impacts.

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 score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.186
Teacher spread0.163 · 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

Citations11
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Food Studies / La Revue canadienne des études sur l alimentationSame topicUrban Agriculture and SustainabilityFrench-language works237,207