Holding space for an Aboriginal approach towards Curriculum Reconciliation in an Australian university
Bibliographic record
Abstract
Since Universities Australia’s Indigenous Strategy recommended a sector-wide approach to ‘closing the gap’ between Indigenous and non-Indigenous Australians, universities have grappled with how to do this. Resisting mainstream approaches to curriculum development that eschew any kind of relational accountability (Wilson, Shawn. 2008. Research is Ceremony: Indigenous Research Methods. Manitoba: Fernwood Publishing) requires entering difficult relations of power and occupying space to transform the act of curriculum development itself. This paper is the second in a series understanding Jindaola, a programme led by a Local Aboriginal Knowledge Holder within one Australian university. It ‘hacks’ the curriculum development space with staff through Aboriginal way towards Curriculum Reconciliation, building knowledge-based relationships between disciplinary and relevant Aboriginal Knowledge. We deliberately and controversially enact this type of relationship, by temporarily bringing the Foucauldian lens of ‘heterotopia’, and interview data from 30 participants, to describe how Jindaola usurps the neocolonial remit to embed Indigenous Knowledge, and creates and holds a counter-hegemonic space to decolonise curriculum development.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.038 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".