Uncomfortable Echoes: Blackfishing First Nations Trauma During COVID‐19
Bibliographic record
Abstract
The concept of enforcing or mandated medical treatment has a history for Aboriginal and Torres Strait Islander peoples that it does not have for those in the broader Australian migrant and settler communities. This involves not just physical sites of hospitals, lockdowns or quarantine camps but also central issues of identity involved with the larger arguments over citizenship and sovereignty. These are important claims of control over others and for what reason or legitimacy. There is a hauntology that persists here for First Nations people and discussions around COVID‐19 management especially in rural and remote areas of Australia must openly acknowledge this upfront. Since 2020, this discussion has involved competing and conflicting medical advice, hyper‐partisan politics and conspiracy theories imported from overseas Sovereign Citizen movements that were not aggressively present during the previous H1N1 pandemic of over a decade ago. As such, this article skirts issues of uncomfortable echoes of medicalised quarantines of the past and uncomfortable alliances between (on the surface) seemingly ill‐fitted groups, using the pandemic years as a case study in blackfishing, astroturfing, and co‐opted grievance.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.030 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| 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".