The Indigenous Digital Divide: COVID-19 and its impacts on education delivery to First Nations university students
Bibliographic record
Abstract
The global COVID 19 pandemic highlighted that the delivery of online education inadvertently disadvantaged Indigenous Australian university students. This situation was particularly critical for Indigenous students from rural and remote locations. Australian universities increased the use of digital technologies to engage, support and teach due to students’ inability to access campuses. This presented universities with challenges in supporting Indigenous students living in and returning to non-urban settings. Often not recognised is the need for better strategies and plans for Indigenous students returning to their rural or remote community to continue their studies due to COVID. These communities often lack suitable infrastructure that would allow access to pedagogical and learning support opportunities. This paper explores how the business decision made by Australian universities to increase reliance on teaching online during COVID impacted the education of Indigenous students. This paper will then canvas ways this ongoing dilemma can be addressed by considering risks, measuring and monitoring performance to guide transformation, including universities’ more inclusive and respectful use of digital technologies involving First Nations people and cultures.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".