Truth‐telling in the Australian Curriculum
Bibliographic record
Abstract
Abstract Unlike Canada and South Africa, Australia has not completed a national Truth‐telling of First Nations histories. As a consequence, the curriculum is at risk of excluding Truth‐telling, leading to indoctrination of past injustices as part of school learning. Our analysis critically examines the use of Truth‐telling language in the Australian Curriculum—Version 9. Eighteen Truth‐telling terms were identified from a chapter on Truth‐telling in the 2018 Joint Select Committee on Constitutional Recognition relating to Aboriginal and Torres Strait Islander Peoples . Using Bernstein's strong and weak classification, instances of Truth‐telling terms were identified in the curriculum. There were three instances of Truth‐telling in the mandated Content Descriptors of discipline‐based learning areas. Only one of these instances was in the primary years. Across the weak classification where teaching was optional, there were 31 instances in the Content Elaborations, one instance in the Cross‐Curriculum Priority and no instances in the General Capabilities. And 16 of the 32 instances in the Content Elaborations were in secondary History which not all students study. With only weak classification of Truth‐telling, students will continue to be indoctrinated into an unconscious learning of bias and erasure of First Nations histories. One way to limit the settler colonial violence in the Australian Curriculum is to mandate more Truth‐telling to overcome what is perpetuating a Great Australian Silence.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".