Centring First Nations knowings in the literature review: a focus on decolonising law curriculum
Bibliographic record
Abstract
Decolonising university curriculum is not a unique Australian project but involves all settler colonial nations in efforts to deconstruct the ‘pathological effects’ of colonisation. This undertaking supports a societal transition towards a more socially just world where the human rights and Knowledges of First Nations are recognised and respected. This paper contributes through an unorthodox integrative review of eight articles reporting embedment of Indigenous Knowings in Australian law curriculum by applying a relationally responsive standpoint. Two authors interpret the literature reviewed and provide a White non-Indigenous perspective of a ‘colonial garden’ and an Indigenous Wiradyuri Wambuul standpoint of relationship with Country. As such, the paper offers an iterative diversity that supports decolonising the curriculum as a shared goal.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".