Climate Change and Education in Canada: A Critical Discourse Analysis of 3 Provinces
Bibliographic record
Abstract
As climate change (CC) continues to develop as an existential threat to humanity and the wellbeing of our planet, many suggest turning to K-12 education as a key factor in mitigation processes. The effects of CC are quickly overwhelming our systems, including but not limited to agriculture, healthcare, infrastructure, biodiversity, and migration. Ergo, we need education that does not limit teaching CC to the sciences and equally confronts our relationship to the environment to prepare a climate literate society. K-12 education possesses great potential to change the status quo as it can perpetuate culture or challenge it. In K-12 education in Canada, CC is often relegated to the sciences and is rarely taught as anthropogenic, mitigable, and misinformation is at times present in classrooms. Limited research has been done on CC and educational policy in Canada with studies mostly examining pre-service teacher training or surveying curricula and textbooks. This research uses critical discourse analysis to evaluate the curriculum and policy documents of three provinces, Ontario, Saskatchewan, and New Brunswick. This project seeks to determine from which perspective these culturally and economically varied provinces are mandated to teach from, evaluate the similarities and differences, and compare this to the United Nations’ recommended framework of sustainability education (a framework that utilizes diverse learning methods to develop critical thinking, social awareness, and an appreciation for the environment) to explore how provinces and territories across Canada can prepare a more climate literate populace to confront our changing climate.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".