A review of food asset maps in Canada
Bibliographic record
Abstract
Food asset mapping is gaining prominence in Canada as an important planning tool for the evaluation of local food systems. In addition to being used by planners to identify opportunities for improved food security, food asset maps are also valuable references for sourcing food locally, particularly by people experiencing food insecurity. Seventy-three food asset maps were reviewed and categorized based on the types of food assets included as well as design characteristics. Built environment assets such as grocery stores and food banks were included in most maps, as were agriculture-based natural food assets like farms, community gardens, and orchards. However, representations of Indigenous-focussed food assets and natural food assets that are not agriculture-based, such as forests, water bodies, and foraging areas, were generally lacking. The lack of representation of Indigenous perspectives on what is considered a food asset reinforces the values of a settler-colonial food system in food asset maps. The methods for food asset mapping therefore need to be changed from current quantitative practices that largely rely on secondary data sources led by governments and non-profit organizations to collaborative approaches that centre the perspectives of Indigenous peoples and other equity deserving groups.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.043 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".