The Potential Use of Agroforestry Community Gardens as a Sustainable Import-Substitution Strategy for Enhancing Food Security in Subarctic Ontario, Canada
Bibliographic record
Abstract
The high prevalence of food insecurity experienced by northern First Nations partially results from dependence on an expensive import-based food system that typically lacks nutritional quality and further displaces traditional food systems. In the present study, the feasibility of import substitution by Agroforestry Community Gardens (AFCGs) as socio-ecologically and culturally sustainable means of enhancing food security was explored through a case study of Fort Albany First Nation in subarctic Ontario, Canada. Agroforestry is a diverse tree-crop agricultural system that has enhanced food security in the tropics and subtropics. Study sites were selected for long-term agroforestry research to compare Salix spp. (willow)-dominated AFCG plots to a “no tree” control plot in Fort Albany. Initial soil and vegetative analysis revealed a high capacity for all sites to support mixed produce with noted modifications, as well as potential competitive and beneficial willow-crop interactions. It is anticipated that inclusion of willow trees will enhance the long-term productive capacity of the AFCG test plots. As an adaptable and dynamic system, AFCGs have potential to act as a more reliable local agrarian system and a refuge for culturally significant plants in high-latitude First Nation socio-ecological systems, which are particularly vulnerable to rapid cultural, climatic, and ecological change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".