Australian First Nations response to the pandemic: A dramatic reversal of the ‘gap’
Bibliographic record
Abstract
Until the recent death in Dubbo of an Aboriginal man, there have been no deaths from Covid 19 in Australia. The extraordinary success of Aboriginal and Torres Strait Islander populations in controlling the effects of this pandemic has been a global role model. Until early 2021, in spite of their high risk status, dispersed population and fear of health services due to racism, Indigenous outcomes were better than those for non-Indigenous. Aboriginal health leaders at every level brought in worlds best practices and applied them in all urban, rural and remote locations. Instead of the many hundreds of cases, hospitalisation and deaths expected, there were only 150 cases nationwide with15% hospitalised but no one in ICU and no deaths. This result is a complete reversal of the gap and was due to the outstanding Indigenous leadership, that governments at all levels listened to Aboriginal wisdom and that control was handed to those who knew what to do. This result is not only evidence for why a Voice enshrined in the Constitution would work, it heralds a new way of working with Aboriginal people in Australia. This viewpoint makes the case for a different model to engage and empower First Nations to really close the gap - themselves.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".