Roundtable: Technologies and Trials of Global Health in Africa
Bibliographic record
Abstract
Global health increasingly promotes technical and entrepreneurial approaches to a wide range of health problems.A variety of actors; governments, clinicians, researchers, NGOs, philanthropic institutions, and corporations, are employing new and re-purposed technologies to tackle diverse issues such as maternal mortality, family planning, childhood nutrition, malaria diagnosis and cancer prevention.The panelists in this roundtable will describe and discuss a range of technologies; biomedical devices, pharmaceuticals, mobile communication platforms and devices, as well as clinical trials and protocols -that are filling the new mandate for simple, high impact, and low cost solutions.This roundtable is concerned with the scientific and social lives of such global health trials and technologies, from their histories, and their research and development, to clinical testing, to their implementation in the field, as well as their impact on the lives of people for whom they are intended over time.With a focus on African settings, the roundtable contributors may also concern themselves with the politics and social debates produced through the global health turn to technology.
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.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.065 | 0.017 |
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".