Bibliographic record
Abstract
In Canadian schools, food and drink offerings have yet to be explicitly incorporated in most inclusive food policies and practices. With the growing number of vegetarian and vegan (veg*n) families and literature documenting their marginalisation, this study sought to explore parents’ experiences within Ontario schools for their children who eat plant-based diets, yet also unearthed how limited choices negatively affected the good health and well-being of lactose and gluten intolerant students, as well as other dietary needs. Taking a narrative inquiry-inspired approach, interviews were conducted with 11 parents. In most schools, veg*n options were not available, and as a result, students felt socially excluded and, at times, were physically left out, which adversely affected their well-being and interrupted their learning. Most parents also felt unsupported when advocating for their children. All parents recommended partnerships for the goals of inclusion and quality education, by forming inclusive school food policies, teaching about a range of diet diversity, including why people adopt these, such as for health, animal welfare, sustainability, religion, culture, and more. By informing school food policies with parent voices, students can benefit from reduced inequalities and good health and well-being. Doing so would foster peace, justice and strong institutions.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".