Educating the Imagination: Teaching for Sustainability through a Bioregional Literacy Curriculum
Bibliographic record
Abstract
In Canada, a national framework proposes to integrate learning outcomes into existing K-12 curricula to teach the values, skills and behaviours of sustainability. This article describes a research project designed to identify existing curricula that may contribute to education for sustainable development (ESD). The province of Newfoundland and Labrador, Canada, has a tradition of developing bioregional curriculum resources that reflect the unique landscape, history and culture of the province. This article presents research investigating the Newfoundland curriculum to determine to what extent it correlates with, and teaches to, the values of ESD as represented by the Decade for Education for Sustainable Development (2005–14) initiative. High school students involved in a four-week unit of study were carefully observed, and the texts they generated were analysed for themes of pedagogical value in an attempt to determine the potential of the Newfoundland bioregional curriculum to foster values associated with ESD. It was found that literacy curricula containing imaginative, creative texts can be used to deepen student awareness of the cultural and living landscapes in which they dwell. Further research is needed to support the use of existing curricular resources and the development of new bioregional curricula specific to unique cultures, communities and bioregions across Canada and other countries.
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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 | 0.001 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".