Infectious Disease in Times of Social and Ecological Change
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 ACKNOWLEDGMENTS This editorial is the outcome of collaboration between the co-authors at the World Health Organization's Special Programme for Research in Training in Tropical Diseases (TDR/WHO). TDR is a specialized agency of the United Nations that has a 36-year history of social research and capacity building on infectious diseases of poverty (WHO 2007 World Health Organization 2007 Making a Difference: Thirty Years of Research and Capacity Building for Tropical Diseases . Geneva : World Health Organization, UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Disease. [Google Scholar]). Additional informationNotes on contributorsChristopher AlleyCHRISTOPHER ALLEY is a medical anthropologist and PhD candidate with Columbia University's Mailman School of Public Health in New York, and is currently finalizing a doctoral thesis on eco-bio-social and political dimensions of dengue fever and urban sanitation in Brazil.Johannes SommerfeldJOHANNES SOMMERFELD is a scientist and research manager with the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases, World Health Organization, Geneva, Switzerland.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 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