Exploring the potential of food forestry to assist in ecological restoration in North America and beyond
Bibliographic record
Abstract
Food forests—edible, perennial, polyculture systems—are of increasing interest in North America and the United Kingdom, as reflected in projects ranging from urban food initiatives to integrated conservation and restoration planning. To examine emerging food forestry (FF) against the backdrop of ecological restoration (ER), we conducted semi‐structured interviews with eight experts each from the fields of FF and ER in conjunction with observations of food forests in Canada, the United States, and the United Kingdom. Using content analysis, our study builds a FF model that encompasses the underlying goals of emerging FF—forest function; diversity of yields; education and culture sharing; healthy habitats for people and other species; and sustainability. We argue that FF has potential as an urban restoration tool in terms of enhancing the multifunctionality of heterogeneous landscapes undergoing significant changes. This will require meaningful consideration of ethical issues (e.g. commodification of nature), landscape contexts, ecological integrity, integration of historical knowledge, and resilience for interdependent, dynamic social and ecological systems. Moreover, systematic, long‐term monitoring of different types of food forests will be crucial in order to mindfully apply FF in ER. This research provides one of the first in‐depth analyses of how emerging FF might contribute to restoration in the time of the Anthropocene, especially outside traditional tropic regions where most FF has been practiced.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".