Serving Up Local: Economic Assessment of Local Food in Long-Term Care Homes
Bibliographic record
Abstract
Since 2016, the Golden Horseshoe Food and Farming Alliance has been working with Ontario’s Long Term Care sector on food purchasing and quality reform through the Serving Up Local project portfolio. The Serving Up Local I research with project teams in 9 municipally-run homes in Durham and Halton Regions and the City of Hamilton, showed that LTC homes could increase resident satisfaction and improve residents’ and their families' perception of the quality of food, by increasing and identifying Ontario-grown and raised foods on the menu. In partnership with the University of Guelph and with financial support through the Ontario Agri-food Innovation Alliance, the original Serving up Local project entered a second phase, with the original partner communities, plus homes in Peel Region, to explore the impacts of local purchasing and tracking priorities. The results of this research project provide an understanding of what is happening in LTC food tracking now, and the greater potential that exists. The research offers evidence of food category tracking as a way to support the Ministry of Long-Term Care’s response to food reform, provide transparency on food quality, and further accountability to Ontario taxpayers on public dollar spend. Funding: OMAFRA through the Ontario Agri-Food Innovation Alliance
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".