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Record W4403804216 · doi:10.1080/13603116.2024.2413515

Improving inclusive school food practices with parent voices

2024· article· en· W4403804216 on OpenAlexaffabout
Julie Morin

Bibliographic record

VenueInternational Journal of Inclusive Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsLakehead University
Fundersnot available
KeywordsPedagogyPsychologyMainstreamingInclusion (mineral)SociologySpecial educationGender studies

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.379
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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