HIV Research for Prevention 2018: From Research to Impact Conference Summary and Highlights
Bibliographic record
Abstract
The HIV Research for Prevention (HIVR4P) conference is dedicated to advancing HIV prevention research, responding to a growing consensus that effective and durable prevention will require a combination of approaches as well as unprecedented collaboration among scientists, practitioners, and community workers from different fields and geographic areas. The conference theme in 2018, "From Research to Impact," acknowledged an increasing focus on translation of promising research findings into practical, accessible, and affordable HIV prevention options for those who need them worldwide. HIVR4P 2018 was held in Madrid, Spain, on 21-25 October, with >1,400 participants from 52 countries around the globe, representing all aspects of HIV prevention research and implementation. The program included 137 oral and 610 poster presentations. This article presents a brief summary of highlights from the conference. More detailed information, complete abstracts as well as webcasts and daily Rapporteur summaries may be found on the conference website.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.105 | 0.033 |
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".