HIV/AIDS stigma and discrimination: a canadian perspective and call to action
Bibliographic record
Abstract
Canada as a nation is committed to addressing HIV/AIDS stigma and discrimination. The federal government has recently announced that funding for HIV prevention, care and treatment will double by 2009, from a current $42.2 million to $84.4 million. While the prevalence of HIV/AIDS in Canada is relatively low, experiences of HIV/AIDS stigma and discrimination are common. In response to this situation, the Canadian HIV/AIDS Legal Network has recently released a report outlining a series of goals and actions designed to prevent, reduce and eliminate HIV/AIDS stigma and discrimination. By promoting tolerance and understanding through research, legislation and community level action we can diminish the overarching epidemic of stigma and discrimination and decrease the extent of the HIV epidemic in Canada.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.061 | 0.047 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".