Bibliographic record
Abstract
Outraged by the lack of concerted effort to fight the AIDS crisis, queer activists formed the direct‐action AIDS organization ACT UP in 1987. They were enraged because tens of thousands of people, disproportionately gay and bisexual men, had died of AIDS, yet governmental, mass media, medical establishment, and other institutional responses were characterized by homophobia and genocidal neglect. Beginning with the first chapter in New York City in March 1987, activists soon established over 80 chapters in the United States, including Puerto Rico, and over 30 elsewhere, including Australia, Canada, and Europe. ACT UP's repertoire of innovative, confrontational tactics was decisive in publicizing myriad aspects of the HIV/AIDS crisis, increasing government HIV/AIDS budgets, preventing new HIV infections, galvanizing research, reshaping clinical drug trials, securing access to services and treatment for people living with HIV/AIDS, and fighting repressive legislation including calls to quarantine people with HIV/AIDS. ACT UP empowered people living with HIV/AIDS and challenged homophobia, racism, sexism, and other inequalities fueling the AIDS crisis. In the process, the organization ignited gay and lesbian pride, cultivating rebellious queer identities and political action that celebrated sexual difference and envisioned a more just and inclusive world. Most ACT UP chapters declined in the early to mid‐1990s but a few continue to do vital AIDS activism today.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.134 | 0.042 |
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".