Bibliographic record
Abstract
POZ Best in Literature Award Canada has been known as a hot spot for HIV criminalization where the act of not disclosing one’s HIV-positive status to sex partners has historically been regarded as a serious criminal offence. Criminalized Lives describes how this approach has disproportionately harmed the poor, Black and Indigenous people, gay men, and women in Canada. In this book, people who have been criminally accused of not disclosing their HIV-positive status, detail the many complexities of disclosure, and the violence that results from being criminalized. Accompanied by portraits from artist Eric Kostiuk Williams, the profiles examine whether the criminal legal system is really prepared to handle the nuances and ethical dilemmas faced everyday by people living with HIV. By offering personal stories of people who have faced criminalization first-hand, Alexander McClelland questions common assumptions about HIV, the role of punishment, and the violence that results from the criminal legal system’s legacy of categorizing people as either victims or perpetrators. Note: A regrettable error appears on page 22. The number 240 should be 206 when referring to the number of people prosecuted in relation to allegations of HIV nondisclosure. This will be fixed in future reprints.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".