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Record W4378650772 · doi:10.7202/1099989ar

Climate Change Education within Canada’s Regional Curricula: A Systematic Review of Gaps and Opportunities

2023· review· en· W4378650772 on OpenAlexaffvenueabout
Ellen Field, Gia Spiropoulos, Anh Thu Nguyen, Rupinder Kaur Grewal

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsMount Royal UniversityLakehead University
Fundersnot available
KeywordsCurriculumInclusion (mineral)Climate changePolitical scienceCurriculum developmentSociologyPedagogySocial science

Abstract

fetched live from OpenAlex

This paper reports on curriculum analysis of climate change expectations in Canada’s provincial curricula. The research is focused on curriculum policy in Canadian provinces; however, it pertains to an international audience as Article 12 of the Paris Agreement, the international treaty on climate mitigation, adaptation and finance, calls for signatories to “enhance climate change education,” and the United Nations Educational, Scientific and Cultural Organization (UNESCO) have called for environmental education to be a core curriculum component by 2025, which will require all countries to evaluate and improve their curricula globally. Curriculum policy within Canada has not yet been aligned with these policy calls, and our analysis showed fractured and uneven inclusion of climate change. Data findings present explicit climate change education curriculum expectations for each province according to grade, subject, and mandatory versus elective courses. The review shows uneven inclusion of climate change topics, themes, and units within grade 7 – 12 curricula, with most expectations occurring in elective senior secondary courses. A second level of analysis with a ranking tool indicates shallow inclusion. The paper concludes with recommendations for addressing gaps.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.601
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.076
GPT teacher head0.352
Teacher spread0.277 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations47
Published2023
Admission routes3
Has abstractyes

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