HIV on the move: Sex differences in patterns of migration and HIV in South Africa
Bibliographic record
Abstract
I’d like to thank my advisor, Rachel Snow, and the other members of my dissertation committee, Yu Xie, Jim Levinsohn and Mark Padilla, for their critical guidance in the development of this dissertation research. I am very fortunate to have benefitted from their combined expertise. This dissertation study was carried out with the support of the Africa Centre for Health and Population Studies in South Africa. I am grateful to the people in Umkanyakude District who have participated in the Africa Centre’s studies, and who are working hard to build better lives for themselves and their families in the midst of the devastation wrought by poverty and AIDS. This dissertation is dedicated to them. I thank Marie-Louise Newell, Director of the Africa Centre, for her advice, guidance and support for this research. I also thank members of the Africa Centre’s scientific team who have collaborated with me in the development of this research, especially Vicky Hosegood, Nuala McGrath and Til Barnighausen. This project wouldn’t have happened if Vicky and Mark Hunter of the University of Toronto hadn’t ignited my interest in studying migration, gender and HIV/AIDS in South Africa several years ago. Tanya Welz and Joerg Baetzing-Feigenbaum provided early guidance with working with Africa Centre HIV surveillance data, and Caterina Hill gave me an excellent primer on the ACDIS data system, for which I remain grateful. I am immensely grateful for the funding I have received in support of this research. The
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".