Bibliographic record
Abstract
This chapter considers the breadth, volume, diversity and appeal of works of literature in English which have engaged with the medical syndrome AIDS - Acquired Immune Deficiency Syndrome - since the first evidence of its spread in 1981. It cannot hope to be representative in terms of the global impact of the AIDS epidemic for a simple reason. Though the scale of HIV/AIDS in many non-Western societies is far greater than in Europe or America, non-Western literary and cultural manifestations of AIDS have been scarce. Equally, though many non-English cultures have seen an impressive, substantial body of AIDS-related literature emerge, I have focused on texts written or made in English. With the exception of a few French, Italian and Spanish authors and filmmakers - Cyril Collard, Hervé Guibert, Pier Vittorio Tondelli, Ferzan Ozpetek, Pedro Almodovar and Juan Goytisolo - non-English texts have had a small circulation, rarely being translated or distributed in the English-speaking world. This relative invisibility holds true for works in English published in countries outside the USA, UK, Ireland and Canada, such as South Africa, Australia or New Zealand. For reasons of space, I have restricted myself mostly to British and American texts in a number of genres, which illustrate key tendencies, and to some lesser-known or untypical works, as well as to some critical and theoretical responses, both to the syndrome and to its cultural representations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".