Bibliographic record
Abstract
Social assistance has proven to be insufficient for low-income families to meet all their basic needs, and covid-19 has further impacted the ability for households to be well fed and healthy. The federal and provincial governments have promised increased funding for agricultural sectors in hopes to increase food security infrastructure. However, large-scale farm operations require an extraordinary use of natural resources, thus questioning the sustainability of industrial agriculture in meeting food security demands at a local level. Thereby exemplifying why alternative policy strategies must be explored due to the problem of affordability amplified by covid-19, as well as the sustainability of agriculture activities in the era of climate awareness. This article argues the potential of a provincial school gardens policy which support cities using urban agriculture within public k-12 school properties to decrease the incurrence of household food insecurity, while also simultaneously adapting to educating youth on their role as stewards to the environment. Furthermore, urban agriculture has the potential to effectively restore farmland lost from urban sprawl by creating gardens that act as a social security net for individuals suffering from food insecurity. Food banks have seen a significant increase in demand for their services in the last decade due to the heightened cost of living. Thus, by having a source of food production that is specifically created as another form of social security, relieves the pressure food banks endure by providing food directly to communities suffering from food insecurities. A variety of programs exist in local cells across canada that can be used as inspiration for the government of ontario to implement a provincial school gardens policy program, however due to the diversity of school layouts, the challenges of implementing gardens at different scales will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.321 | 0.073 |
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".