Bibliographic record
Abstract
Acknowledging First Nations perspectives in Australian primary schools is driven by an interplay of factors which specifically rely on teacher capability and confidence. Ministerial education declarations, mandated curriculum documents, and political debates surround the ongoing arguments around what should and shouldn’t be taught in history curriculum in Australian primary schools. Drawing on a temporal lens, this chapter explores the author’s own school experiences of learning First Nations perspectives in the 1980s and 1990s and reveals how ‘teacher memories’ can reiterate colonised practices if not disrupted and deconstructed. Adding to this is the challenge of decolonising curriculum when teachers who are culturally unfamiliar, underprepared and under-resourced face the prospect of not teaching First Nations perspectives at all. The chapter concludes with practical ideas and strategies for empowering school leaders and teachers to deconstruct colonisation in their schools by introducing the ‘Maroondah Framework’ (Wurundjeri- Woi Wurrung word for ‘throwing leaves’) consisting of Yarning (storytelling), Dadirri (deep listening) and Ganma (knowledge sharing) as a way for schools to consider future opportunities to collaboratively work towards decolonising History curriculum and school environment contexts.
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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 | 0.002 | 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 | 0.013 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".