Bibliographic record
Abstract
Tuberculosis has historically thrived in some of the most impoverished and marginalized populations; however, research into why lower socio-economic populations are at a higher risk of contracting diseases remains to be a topic lacking exploration. This is becoming a more prevalent issue as multi-drug resistant Tuberculosis (MDR-TB) and extensively-drug resistant Tuberculosis (XDR-TB) are on the rise in individuals living in poverty. The main research question that will be addressed in this paper is: How do socio-economic inequalities play a role in rising incidence rates of Tuberculosis in South Africa, and what are the future challenges in dealing with MDR-TB and XDR-TB? South Africa is a particularly important country of focus because of the racial inequity that has resulted in economic disparity, and a large percentage of the population living below the poverty line. Reviews of existing literature on Tuberculosis and correlations to poverty will be critically analyzed and applied, as well as the use of government documents, including Statistics South Africa, and the World Health Organization. A cross-cultural comparison of Canada and South Africa will be included to highlight the long-term effects of marginalization societal stratification. The health care policies dealing with treatment will also be discussed, with a specific focus on social epidemiology. This paper will argue that long-term racial inequalities in South Africa has result in economic disparity, through which Black Africans and people of Colour are more susceptible to contracting TB, MDR-TB, and insufficient health resources to support them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".