Do food donation tax credits for farmers address food loss/waste and food insecurity? A case study from Ontario
Bibliographic record
Abstract
To increase donations of nutritious food, Ontario introduced a tax credit for farmers who donate agricultural products to food banks in 2013. This research seeks to investigate the role of Ontario's Food Donation Tax Credit for Farmers in addressing both food loss and waste (FLW) and food insecurity through a case study of fresh produce rescue in Windsor-Essex, Ontario. This research also documents the challenges associated with rescuing fresh produce from farms, as well as alternatives to donating. Interviews with food banks, producers and key informants revealed that perceptions of the tax credit, and the credit's ability to address FLW and food insecurity, contrasted greatly with the initial perceptions of the policymakers who created the tax credit. In particular, the legislators did not anticipate the logistical challenges associated with incentivizing this type of donation, nor the limitations of a donation-based intervention to provide food insecure Ontarians with access to fresh, nutritious food.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".