Bibliographic record
Abstract
When the first author of this chapter coined the term “post-AIDS” and presented a paper on it at the XI International AIDS Conference in Vancouver, 1996, the goal was to frame a growing division in the responses of Australia’s gay communities related to HIV serostatus and diverging priorities in prevention and in care and support for people living with HIV. Some thought then that post-AIDS meant AIDS was over. No, it did not. Others thought it meant an end to HIV as a “crisis.” No, it did not. Some saw it as a precursor to recent slogans such as the “end of AIDS” and an “AIDS-free generation.” No, it was not. Some applied the term to other groups affected by HIV. No, the term referred to Australian gay communities but was quickly taken up in the U.S.A. It is timely to re-assess “post-AIDS” to understand gay men’s relation to HIV and each other; its usefulness to other HIV-affected communities; and its relevance to effective vaccines, an HIV “cure,” and treatment advances. This re-assessment takes place in a global context of nearly 2 million new HIV infections yearly and 40% of those infected still unable to access treatment. AIDS is definitely not over.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".