Decolonial STEM Education and the Integration of a Critical Global Citizenship Education Framework in an Ontario Secondary School
Bibliographic record
Abstract
This study explores how the HEADSUP framework can help STEM teachers integrate anti-oppressive and critical social justice frameworks into their teaching. Using a qualitative case study approach, STEM teachers and students in an Ontario secondary school participated in weekly sessions to learn about utilizing HEADSUP. Data were generated through semi-structured interviews with teachers and observations by the research team. The findings demonstrate the opportunities and challenges of decolonizing STEM education in Canadian schools through critical decolonial frameworks. Interviews with teachers revealed that integrating HEADSUP amplified students' voices and promoted active engagement in STEM classrooms. Teachers also highlighted that the HEADSUP framework helped students connect more fully with the curriculum and encouraged them to explore concepts beyond the STEM curriculum. The findings also show that utilizing anti-oppressive and social justice frameworks in STEM classrooms can be challenging due to constraints imposed by the current provincial curriculum. Implications for policy are discussed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".