“Community Over Commercialization”: Help Us Keep GHSP Open
Bibliographic record
Abstract
ince its inception in 2013, Global Health: Science and Practice (GHSP) has aimed to bridge the gap between knowledge and action in global health programs by embracing diverse forms of knowledge and learning to better understand what works and what doesn't in real-world settings.We also have provided an equitable publishing platform to ensure that knowledge is cultivated and disseminated as a global good, not a commodity.1,2 With financial support from the U.S. Agency for International Development (USAID) and additional, limited support from organizations, such as the Gates Foundation, the Doris Duke Charitable Foundation, and People that Deliver, for publishing supplements, GHSP has remained one of the few global health journals that is truly open access.The journal has not charged any article processing fees to authors, regardless of location, affiliation, or article type, and has not imposed subscription fees on readers.By prioritizing "community over commercialization," 3,4 GHSP has fostered a global community of health practitioners, program managers, decision-makers, and policymakers globally who are better connected to each other and to evidence on what works and under what conditions.GHSP's model has helped to strengthen programs and amplify their impact to facilitate lasting change.Now, this model-and even the journal itself-is at risk.In February 2025, amid the broad terminations of USAIDfunded global health projects, USAID ended its support for GHSP.As the publisher of GHSP, the Johns Hopkins Center for Communication Programs (CCP), along with the editor-in-chief and associate editors, remains committed to maintaining GHSP as an independent journal.CCP is actively exploring ways to achieve this without imposing fees on authors or readers.We know that the journal fulfills an essential role for public health practitioners and researchers worldwide-a role that has become even more pronounced in today's global context.GHSP provides
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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 | 0.079 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.055 | 0.074 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.040 | 0.035 |
| Insufficient payload (model declined to judge) | 0.077 | 0.033 |
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".