Digital Technology and the Political Determinants of Health Inequities: Special Issue Introduction
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.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".