Uncovering hidden urban bounty: A case study of Hidden Harvest
Bibliographic record
Abstract
Urban food systems primarily rely on foods grown in rural spaces, and often face challenges in creating spaces to grow fresh, healthful and affordable food in cities. Urban food harvest organizations aim to overcome these challenges by locating and harvesting food that already exists in cities on the numerous fruit- and nut-bearing trees located on public and private lands. Hidden Harvest is a leading initiative for urban fruit and nut harvesting in Canada, and unique in its for-profit social enterprise model. The organization aims to legitimize and support the practice of harvesting fruits and nuts in urban areas, and provides a means to increase access to—and availability of—fresh, healthful foods hyper-locally in Ottawa, as people harvest from their own (or nearby) neighborhoods. This field report examines the challenges and opportunities faced by Hidden Harvest in attempting to link multiple social, environmental and economic goals relating to food sovereignty, social justice and ecological sustainability. In particular, the organization seeks to establish a self-sustaining business model through innovative solutions and the development of networks with local food processes, food organizations and businesses, which enables Hidden Harvest to grow and develop distinct ties and relationships in Ottawa. This case study reveals how organizations such as Hidden Harvest use food to enhance and tie together local economies, knowledge, food security and community well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".