Using the SDGs for global citizenship education: definitions, challenges, and opportunities
Bibliographic record
Abstract
The 17 United Nations Sustainable Development Goals (SDGs) employ a global indicator framework to detail each Goal and monitor its implementation. This article focuses on three targets from the indicator framework, which call for mainstreaming education for global citizenship, sustainable development, and climate change into national curricula. By investigating the practicalities of meeting these targets from an educator's perspective, this article proceeds with: arguing for a need to shift the central purpose of education; examining what is meant by education ‘for’ the three key areas included in the global indicator framework; exploring curricular opportunities offered by the SDGs; and presenting inquiry-based learning as a pedagogical approach for critically interrogating the SDGs with learners. If the SDGs are used to drive a pragmatic definition of global citizenship, then trends in education such as inquiry- and problem-based learning come to life with a clear and urgent purpose.
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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 | 0.055 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".