What we know about HIV and AIDS in the armed forces in Southern Africa : commentaries
Bibliographic record
Abstract
This paper is a summary of some of the key findings of an eighteen-month MilAIDS research project1 that focused on how militaries in the Southern African countries of Botswana, Swaziland, Tanzania, Zambia and Zimbabwe had coped with the impact of the HIV epidemic since it had been identified amongst the ranks in the 1980s. As a result, there is a single major source for citation, which is 'The enemy within: Southern African militaries' quarter-century battle with HIV and AIDS'. The summary does, however, contain other information related to developments that have emerged since the completion of the larger study, bringing us up to date with the contemporary discourse in the field. The purpose of highlighting some of the elements in the larger study is twofold: to distil its main findings for easier consumption and to draw our attention to salient factors that are considered worthy of replication. A second objective of this brief paper is of course to whet readers' appetite to read the more detailed work referred to above.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".