Hidden Harvest's Transformative Potential: An Example of 'Community Economy'
Bibliographic record
Abstract
Drawing on an in-depth case study of Hidden Harvest Ottawa—a for-profit social enterprise that aims to legitimize and support the practice of harvesting fruits and nuts in urban areas—this article explores the transformative potential (both realized and unrealized) of place-based urban foraging. It briefly delineates the organizational model employed, including its innovative practices and strategic 5-year vision. It then explores Hidden Harvest’s transformative potential realized: notably, it reconceptualizes surplus (and thus profit); makes visible a nonmonetary social return on investment (SROI, defined as substantive contributions to building community, adaptive capacity, prosperity, social capital, and community-based food security); normalizes access to public space for food provisioning; and, finally, frames Hidden Harvest as an illustrative example of Gibson-Graham’s (2006) notions of community/alternative/ethical economy, an initiative that destabilizes dominant economic assumptions while fostering meaningful interconnection. Throughout this article, we argue that only through collective resignification of our economy can initiatives such as Hidden Harvest adequately receive the support warranted by its impact and outcomes to fully realize its potential and achieve long-term viability. See the press release for this article.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".