Fighting TB stigma: we need to apply lessons learnt from HIV activism
Bibliographic record
Abstract
### Summary box In 2015, 10.4 million people were diagnosed with tuberculosis (TB)1 and 2.1 million people tested positive for HIV.2 Over two-thirds of new TB and HIV infections are in lower income and middle-income countries in sub-Saharan Africa and Asia. Together, TB and HIV cause over 2.5 million deaths each year,1 2 and immeasurable social calamities. Key among these is widespread stigmatisation incited by their deep-set association with poverty, social marginalisation, risk of transmission and death, and perpetuated to varying degrees by subversive policies and practices.3 4 The stigma is doubly worse for the 1.2 million people world over who live with both …
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".