Managing the march of COVID-19: lessons from the HIV and AIDS epidemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsAlan WhitesideAlan Whiteside OBE is the chair of Global Health Policy at Balsillie School of International Affairs, Waterloo, Canada, and also professor emeritus at the University of KwaZulu-Natal, South Africa. He wrote his first article on HIV in 1987 and in 1998 established the Health Economics and HIV AIDS Research Division at UKZN. He is the editor-in-chief of the African Journal of AIDS Research. Email: awhiteside@balsillieschool.ca ORCID: https://orcid.org/0000-0003-1157-968XWarren ParkerWarren Parker is an independent public health and communication specialist based in San Diego, USA. He has worked in the HIV field since 1990 and co-founded the Centre for AIDS Development, Research and Evaluation (CADRE) in South Africa in 2000. Email: warrenmparker@mac.com ORCID: https://orcid.org/0000-0003-0765-3613Mike SchrammMike Schramm is managing director of NISC (Pty) Ltd, a South African academic publishing company that produces journals, books and databases that showcase the best of African scholarship. NISC is the publisher of the African Journal of AIDS Research. Email: mike@nisc.co.za ORCID: https://orcid.org/0000-0003-1305-1205
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it