Promoting First Nations understandings of sustainability in both teacher professional development and in undergraduate course learning
Bibliographic record
Abstract
The aim of this chapter is to explore an innovative professional learning and teaching model intended to promote First Nations’ perspectives of sustainability in Australian undergraduate courses. As part of the Yarning to Learn initiative, First Nations university educators facilitate yarning circles with non-Indigenous university educators. In these safe spaces, non-Indigenous university educators were supported to reflect on and evaluate spaces where First Nations’ voices could be heard, or amplified, through sustainability-related concepts. The program was trialled across three Australian universities: Wurundjeri (Naarm/Melbourne), Wadawurrung (Waurn Ponds) and Nyungar Countries (Boorloo/Perth). To the best of our knowledge, this model of professional learning is a first-of-a kind design, embracing yarning and yarning circles as spaces for sharing and reflecting on how to promote First Nations’ perspectives in our teaching. We draw on autoethnographic methodologies to explore both the experiences of the participating First Nations and non-Indigenous university educators. In this chapter, Yarning to Learn acts as a cross-cultural bridge to support decolonising knowledge and thinking relating to sustainability education, facilitating non-Indigenous university educators as they are mentored, supported, and by led by expert First Nations university educators. These initiatives support efforts to empower non-Indigenous university educators to decolonise teaching and promote undergraduates’ understandings of First Nations’ worldviews of caring for Country and associated sustainability-related concepts and practices.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".