Bibliographic record
Abstract
In the summer of 2009, I visited a humble orphanage for children with HIV/AIDS in Vietnam. Here, like many parts in the world, the very existence of marginalized people with stigmatized illness is hidden away. Relegated to the shadows of society, these children lacked something more fundamental than housing, shelter, nutrition and medications. They lacked families to love and care for them unconditionally. One might think it self-evident that a visit to an orphanage for children with HIV would be profound, but the profundity wasn't where I expected to find it. It was in how the children had created their own family, loving each other like brothers and sisters, and the way the priest who operated the shelters was more than a Father, he was a dad to dozens of children. This is an account of love as harm reduction in the Mai Tam orphanage in Ho Chi Minh City.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".