Reimagining Open Textbooks Through a Decolonising Lens: Non-Linear Practices for Holistically Integrating First Nations Knowledges into Curriculum
Bibliographic record
Abstract
This case study describes our project to transform an undergraduate open textbook at La Trobe University. At the time of writing this case study the revised version of the text is forthcoming. Here we share one of our key outcomes: the collaborative process we are using to make the transformative revisions. We reflect on a First Nations-led cultural safety review process that is enabling health science academics and library staff to jointly reconstitute this Open Education Resource (OER) as a culturally responsive text that is inclusive and accessible for diverse learners. We highlight the role of First Nations staff in leading a decolonising agenda and how non-Indigenous practitioners are supporting them through culturally responsive practices. We focus on how the project embodies First Nations ways of knowing, being, and doing through Third Spaces that foster power equity and mutually beneficial two-way learning. These ways of working provide an active alternative to the emotionally based paralysis that commonly affects non-Indigenous people, stemming from a fear of “doing the wrong thing”. This often demobilises their capacity to transform beyond allyship on an individual level (which can be tokenistic or performative and a way to stay comfortable) into ‘accomplices’ striving for wider systemic change, regardless of personal or professional discomfort (Finlay, 2020; Rix et al., 2023). Our approach is part of a broader paradigm shift to integrate First Nations knowledges into higher education curriculum in a meaningful way that is holistic rather than tokenistically additive. This paradigm shift reflects a decolonising approach which requires de-centreing dominant Western perspectives by challenging deeply rooted conventional norms and principles, and re-positioning colonial power in collaborative relationships (Smith, et al., 2018). We conclude our case study with some final reflections as prompts for practitioners to use for normalising culturally responsive practices in the Australian open education movement. We invite open education practitioners to join us on this shared journey. This is a book chapter in the edited volume https://oercollective.caul.edu.au/openedaustralasia/
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".