Race and culture in the secondary school health and physical education curriculum in Ontario, Canada
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore issues of race and culture in health education in the secondary school health and physical education (HPE) curriculum in Ontario, Canada. Design/methodology/approach Using Ontario’s secondary school curriculum as a point of analysis, this paper draws from critical race theory and a whiteness lens to identify how cultural and race identities are positioned in contemporary health education documents. The curriculum document and its newest strategies for teaching are the focus of analysis in this conceptual paper. Findings Within the curriculum new teaching strategies offer entry points for engaging students in learning more about culture and race. In particular, First Nation, Métis and Inuit identities are noted in the curriculum. Specifically, three areas of the curriculum point to topics of race and culture in health: eating; substance use, abuse and additions; and, movement activities. Within these three educational areas, the curriculum offers information about cultural practices to teach about what it means to understand health from a cultural lens. Social implications The HPE curriculum offers examples of how Ontario, Canada, is expanding its cultural approaches to knowing about and understanding health practices. The acknowledgment of First Nations, Métis and Inuit health and cultural ways of approaching health is significant when compared to other recently revised HPE curriculum from around the globe. The teaching strategies offered in the curriculum document provide one avenue to think about how identity, culture and race are being taught in health education classrooms. Originality/value First, with limited analysis of health education policy within schools, the use of critical theory provides opportunities for thinking about what comes next when broadening definitions of health to be more inclusive of cultural and race identity. Second, curriculum structures how teachers respond to the topics they are delivering, thus how HPE as a subject area promotes healthy practices is highly relevant to the field of health education. This paper provides an important acknowledgment of the educative work being undertaken in the revision of HPE curriculum.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".