First Nations’ histories in the Australian curriculum: how the national curriculum is failing to meet international law standards on Indigenous peoples’ rights
Bibliographic record
Abstract
During the Regional Dialogues that preceded the Uluru Convention and the Uluru Statement from the Heart, many delegates reflected on how a truth-telling process could lead to a change in how Australian history is taught in schools. In October 2023, one of the three proposed reforms in the Uluru Statement from the Heart—the establishment of a First Nations Voice enshrined in the Constitution—was voted against by a majority of Australians in a referendum. In an open letter responding to the referendum outcome, a group of First Nations leaders, community members and organisations who supported the Voice, re-iterated calls for truth-telling and the need for better education about Australian history in schools. In light of these calls, and in the context of a documented increase in racism against First Nations people in the lead up to the referendum, it is both timely and important to reflect on what is taught about First Nations histories in Australian schools. This article will explore what is at stake, in terms of Australia’s commitments under international law, if the status quo is preserved and Australia’s school history curriculum remains unchanged.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".