Statistical Footnotes or Suffering Human Beings? Humanising AIDS in Moses Isegawa’s Abyssinian Chronicles.
Bibliographic record
Abstract
Uganda is perhaps the only country in the world that has been able to reduce its AIDS infection rates from about 30% in the late 1980s to 6.1% at the turn of the 21st century. This dramatic reversal has been partly attributed to Uganda’s openness about and/or communicative strategies that promoted abstinence, faithfulness and condom use in the fight against AIDS. While a huge corpus of Ugandan medico-anthropological archive that documents this phenomenon privileges statistical data to foreground the impact of the disease on society, another library — consisting of mainly fictional texts — offers an alternative understanding of the disease by foregrounding the corporeality of AIDS through skilful use of characterisation and setting. The fictional representations of AIDS in the Ugandan public sphere, unlike the medico-scientific renditions that essentialise and reduce the disease and its victims to statistical footnotes, offers readers a personalised understanding of the of the disease that unravels its complex contours. In this article, I centre the idea that fiction as an affectively didactic type of knowledge production in AIDS discourses to argue that it is capable of offering profound insights into complex realities like AIDS. Thus, I explore how Moses Isegawa’s Abyssinian Chronicles fuses third person focalisation with spatial and temporal setting to capture the terrifyingly complex and intricate contours of the plague that other documents elide.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".