Gleaning in the 21st Century: Urban food recovery and community food security in Ontario, Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".