Promoting social justice in the capitalist academy? Health equity and the Johns Hopkins University Michael Bloomberg School of Public Health
Bibliographic record
Abstract
The past two decades have seen increasing mentions of health equity and the importance of addressing the social determinants of health in USA public health statements. Yet, there is little uptake of these concepts into USA public policy. We see this, in part, as being due to the unwillingness of the USA public health community – including its network of Masters of Public Health Programs – to address the fundamental cause of health inequities: the United States’ capitalist economic system which skews the distribution of the social determinants of health in favour of the wealthy and powerful. We illustrate this reluctance by examining how the Bloomberg School of Public Health of Johns Hopkins University conceptualises the promotion of health equity through its International Declaration of Health Rights . Nothing in the Declaration considers how the economic system threatens health yet it is presented as a model for public health education. We review its shortcomings and show how revision to it is unlikely since the School is endowed by its namesake billionaire Michael Bloomberg who has denounced any attempts at redistributing wealth and income in the service of public health. Evidence of how public health messaging is already shaped by powerful economic interests embedded within the United States’ capitalist system substantiates concerns that have been raised about such branding and its effects on public health discourse and action.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".