Beyond the social: Cumulative implications of COVID-19 for first nations university students in Australia
Bibliographic record
Abstract
This position paper explores possible repercussions of the Corona Virus (COVID-19) response for Indigenous Australians in higher education. Focusing on Western Australian universities’ swift migration to online learning, halfway through a teaching semester, we identify risks at the intersection between educational and digital inequities, illustrated by author experiences working in an Indigenous Education Unit. Considering our observations of Indigenous students’ recent experiences, we argue that the pre-existing digital divide in Australia creates challenges for Indigenous university students, in addition to those faced by all university students coping with the transition to online learning in a context of social isolation. These include experiences of 1) cultural isolation, brought about by being – both physically and digitally – cut off from extended family, community, and Country and 2) digital isolation, brought about by inequitable access to the full range of digital infrastructure required for effective online learning. We argue that these intersecting layers of isolation highlight persistent inequities in Australian society and create new challenges for Indigenous university students. We appeal to universities to acknowledge and ameliorate culturally specific forms of isolation for Indigenous students, triggered through the complex combination of COVID-19 anxiety, the digital divide and educational minority status. Without this, we worry that the COVID-19 pandemic could reverse the global trend towards increasing Indigenous participation in university education and set-back efforts to Indigenise the university sector.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".