The Virus Is Not the Only Disease: How Public Health Crises Aggravate Structural Inequities and Further Put Minoritized Groups at Risk
Bibliographic record
Abstract
The objective of this paper was to examine the differential ways minoritized groups and dominant groups are affected by public health crises such as the COVID-19 pandemic. A variety of academic sources were consulted, such as various peer-reviewed journal articles and published books, to determine the impacts that public health crises have on individuals with minority identities. Additionally, given the current nature of this topic, select news sources were also used to inform the most recent policy updates on the issues discussed. Th paper largely focused on examples within the COVID-19 pandemic, but also drew from the Ebola Outbreak in West Africa and the HIV/AIDS epidemic. The paper investigated three major ways in which minoritized groups are disproportionately impacted by public health crises. First, government response measures frequently suit dominant groups much better than minoritized groups, partially because response measures are typically drafted by members of dominant groups and partially because it is much more difficult to comply with many emergency response measures in the absence of privilege. Furthermore, because minoritized groups are often already in positions of socioeconomic disadvantage compared to dominant groups, times of emergency often exacerbate the pre-existing social conditions that cause inequity. The third way the paper found minoritized groups to be disproportionately affected by public health crises was that tensions towards perceived “at fault” groups and tensions between racialized minorities and authorities become strained. Lastly, the paper found that while education may be a partial solution to these issues, it is not a full solution.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".