Embedding Indigenous knowledges in the design of higher education curriculum:An international study in law education
Bibliographic record
Abstract
This thesis aims to develop an original model to assist universities to embed Indigenous knowledges into the higher education law curriculum. The model provides guidance to universities on forming collaborative partnerships with Indigenous peoples and communities. In Australia, a pedagogical matrix developed by Ranzijn, McConnochie, and Nolan (2009) is currently used to guide the inclusion of Indigenous perspectives in curriculum; and a directive from the Universities Australia Indigenous Strategy 2017–2020 states that universities need to ‘have processes that ensure all students will encounter and engage with Aboriginal and Torres Strait Islander cultural content as integral parts of their course of study by 2020’ (Universities Australia, 2017, p. 14). However, to date limited research has focused on the process of developing collaborative partnerships between Indigenous peoples and academia to support the ethical implementation of processes that embed Indigenous knowledges into higher education curricula. A collaborative partnership between academia and Indigenous peoples is considered an essential requirement for the authentic inclusion of Indigenous knowledges in the higher education curriculum (Battiste, 2017;Behrendt, Larkin, Griew, & Kelly, 2012; Kovach, 2009a, 2009b; Smith, 1999;Styres, Zinga, Bennett, & Bomberry, 2010; Universities Australia, 2017). This study contends that drawing on informed knowledge from national and international perspectives about the process of embedding Indigenous knowledges in the higher education curriculum is of great importance for universities in Australia to meet the aims of the Universities Australia Indigenous Strategy 2017–2020. As such, the study will examine literature and practice from one national case study and one international case study to inform Australian institutional practice. The study will employ a three-phased research methodology. The first phase of the research identifies challenges and needs within both the Australian and international literature in relation to embedding Indigenous knowledges in higher education. The second phase explores the use of Ermine’s (2007) Ethical Space Theory as a theoretical basis for examining the identified themes that emerge from the literature. The third phase examines the practices across Charles Sturt University, Australia, and the University of Victoria, Canada, both of which include Indigenous knowledges in the higher education law curriculum.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".