Bibliographic record
Abstract
Globally, human health is improving. Aggregate world health data indicate enormous improvement over the last 100 years. Life expectancy, vaccination, and sanitation rates are higher. Rates of infectious disease, HIV/AIDS, child and maternal mortality are lower. These gains have all been accomplished during a time when governments orchestrated, or aspired to provide, albeit often imperfectly, the systemizations of health care. Now austerity and privatization campaigns shape health services worldwide, and we witness a massive ideological shift in approaches to the world’s wickedest health problems: Public health endeavors must ‘show return on investment.’ This commentary takes up activities in three health domains where effort goes into the appearance of global health prowess and accomplishment: health security; health innovation; and health finance. Pseudo global health, as an analytic, helps us take measure of the in-between phenomenon between real and fake accomplishment, success and failure, improved health outcomes and continued suffering. I show: 1) how global health security documents sometimes serve as ‘alibis’ – that is, contrivances offered to intimate local readiness or safety despite their actual absence; 2) how global health innovation influencers often privilege tech-fixes developed far removed from real-time people, places, and practices; and 3) how global health finance has already evolved in ways that makes suffering profitable. The examples are meant to enlighten rather than depress and are offered with the hope that critical analyses using the pseudo health concept as an analytic prompt new strategies for sustainable changes rather than merely their appearance.
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 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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".