A Food-Circular Economy-Women Nexus: Lessons from Guelph-Wellington
Bibliographic record
Abstract
Resource nexus approaches have been expanding to include additional sectors beyond standard water, energy, and food approaches. Opportunities exist by re-imagining the resource nexus approach with the framework of the United Nations Sustainable Development Goals (SDGs). Emerging research and policy themes, such as the circular economy and gender, can provide additional context to traditional nexus arrangements. To illustrate this, we analyze SDG implementation and interaction from 40 unstructured interviews from SMEs participating in Guelph-Wellington’s Seeding Our Food Future (SOFF) program, part of the wider Our Food Future (OFF) initiative led by the City of Guelph and Wellington County in Ontario, Canada. Results show that 16/17 SDGs and associated targets were present on the program. Environmental SDGs were implemented the most, followed by social and economic ones. SDGs 2, 12, and 5 had the most general implementation and direct paired interactions and were associated with the broadest number of SDGs across the project. These findings support the existence of a Food-Circular Economy-Women nexus in Guelph-Wellington’s agri-food sector. Further analysis shows that this nexus is most active in agriculture, and that women are responsible for introducing a social aspect, which addresses food security. Results can inform food system and circular economy researchers and practitioners.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| 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".