COVID‐19 stigma, Australia and slow violence: An analysis of 21 months of COVID news reporting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Throughout the peak periods of Australia's COVID‐19 pandemic experience of 2020–2021, some lower socioeconomic areas with high migrant populations in Sydney and Melbourne were subjected to strict lockdown enforcement that included heavy police surveillance not experienced in the more affluent areas of these cities. Analysis of four major newspapers' COVID‐19 coverage from January 2020 to September 2021 reveals their reporting focussed on aspects of individual behaviour that inferred people from lower socioeconomic areas had a heightened risk of exposure to COVID‐19. We argue this media portrayal of severe lockdowns and policing measures played a prominent role in exacerbating previously existing stigma as it compounded the stigma levelled at those communities. It further contributed to an “us and them” mentality that aligned with persistent structural inequality to compound existing stigma to develop a slow violence of COVID‐19‐related stigma. The lens of slow violence allows us to track incremental stigmatising processes, often imperceptible when viewed in isolation. Axiomatic violence adds a complementary perspective to slow violence, highlighting the role of crisis and disciplinary behaviours in embedding structural violence into the social norms of daily life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it