Digital Technology and the Political Determinants of Health Inequities: Special Issue Introduction
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
Abstract This special issue introduction makes the case for analyzing the rise of digital health technologies within global public health within the framework of political determinants of health and identifying how digital technologies impact, both positively and negatively, inequities in health. This special issue brings together diverse perspectives from academics, policy makers, practitioners and activists from around the world, most of whom participated in a 2019 conference Political Origins of Health Inequities: Technology in the Digital Age . The contributions engage with empirical data and practical experiences from Africa (Ghana, Tanzania, Kenya, South Africa, Sierra Leone), Asia (India), Europe (Germany, Norway, the European Union), and North America (the United States and Canada). Taken together and individually, the six research articles, seven ‘policy insight’ commentaries and three ‘practitioner commentaries’ identify and critically interrogate the political dimensions that link digital technologies and health equity.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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