Decolonisation through Poetry: Building First Nations’ Voice and Promoting Truth-Telling
Bibliographic record
Abstract
The impetus to decolonise high schools and universities has been gaining momentum in Southern locations such as South Africa and Australia. In this article, we use a polyvocal approach, juxtaposing different creative and scholarly voices, to argue that poetry offers a range of generative possibilities for the decolonisation of high school and university curricula. Australian First Nations’ poetry has been at the forefront of the Indigenous political protest movement for land rights, recognition, justice and Treaty since the British settlement/invasion. Poetry has provided Aboriginal and Torres Strait Islander peoples with a powerful vehicle for speaking back to colonial power. In this article, a team of Indigenous and non-Indigenous researchers argue that poetry can be a powerful vehicle for Indigenous voices and Knowledges. We suggest that poetry can create spaces for deep listening (dadirri), and that listening with the heart can promote truth-telling and build connections between First Nations and white settler communities. These decolonising efforts underpin the “Wandiny (gathering together)—Listen with the Heart: Uniting Nations through Poetry” research that we discuss in this article. We model our call-and-response methodology by including the poetry of our co-author and Aboriginal Elder of the Kungarakan people in the Northern Territory, Aunty Sue Stanton, with poetic responses by some of her co-authors.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".