‘When whites catch a cold, black folks get pneumonia’: a look at racialized poverty, space and HIV/AIDS
Bibliographic record
Abstract
This paper explores the lived experiences of former NBA player Magic Johnson, and the late ‘Godfather of Gangsta Rap’ Eazy E to examine how their everyday realities as Black men with different socio-economic opportunities around the Civil Rights era affected their fight against HIV. Johnson contracted HIV nearly 30 years ago, and continues to live a healthy, productive life. Eazy E on the other hand, contracted the virus around the same time and later succumbed to AIDS. The differences in the lived experiences of the two men warrant scholarly attention, particularly now amidst the Covid-19 pandemic. Their differences in social position, stemming from the uneven inequities of the culture and racialization of poverty, much like the wider global epidemic of HIV/AIDS itself, are crucial in the spread and survival rate of those that contract HIV. Overall, then, this paper aims to address the following research question: how do social issues of space and racialized poverty affect the lived experiences of African Americans with HIV? This paper will examine the production of social space and spatial structural violence, as well as racialized poverty, and their effects on likelihood of infection and survival of HIV and infectious disease more broadly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".