Bibliographic record
Abstract
Despite unprecedented global wealth creation, health inequity-the unjust health inequality between classes and groups among and within countries-persists, reviving the relevance of social justice as a lens to understand and as an instrument to intervene in these issues. However, the theoretical aspects and polysemous character of social justice as applied in the field of public health are often assumed rather than explicitly explained. An intersectional justice approach to understanding health inequality, inequity, and injustice might be useful. It argues that preexisting class-, race/ethnicity-, and gender-based health injustice and the socially differentiated impacts of the COVID-19 pandemic are shaped, interconnectedly, by economic maldistribution, cultural misrecognition, and political misrepresentation. Pursuing health justice requires analyses, strategies, and interventions that integrate the economic, cultural, and political spheres of redistribution, recognition, and representation, respectively. Such an intersectional approach to health justice is even more relevant and compelling in light of the COVID-19 pandemic. This article is broadly about class, race/ethnicity, and gender political economy of public health-but with a narrower focus on maldistribution, misrecognition, and misrepresentation, shaping social and health injustices.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".