Single-issue advocacy in global health: Possibilities and perils
Bibliographic record
Abstract
There is no thing as a single-issue struggle because we do not live single-issue lives"-Audre LordeIn 2014, during the height of the West African Ebola crisis, Liberian nursing assistant Salome Karwah was profiled on the cover of Time Magazine as person of the year.Three years later, Time Magazine reported that Ms. Karwah had died in childbirth [1].Ms. Karwah's survival from Ebola, but tragic death from childbirth complications, is not a unique case.During the Covid-19 pandemic, many people who survived the virus died from other diseases and complications as health systems became strained or collapsed [2].Every primary health care worker will have a story to share about a child surviving measles or malaria thanks to vaccines or timely emergency care, but suffering from or succumbing to malnutrition, violence, or diarrhea.
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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 | 0.176 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.023 | 0.055 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.045 | 0.035 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".