What Is<i>Global Health: Science and Practice</i>Doing to Address Power Imbalances in Publishing?
Bibliographic record
Abstract
W hat is labeled "global health" has largely con- cerned the practice of public health work elsewhere, generally in low-and middle-income countries (LMICs). 1 Indeed, global health's key feature is that its power structures are generally located in high-income countries (HICs) while its implementation is generally located in LMICs.This imbalance is a result of colonial history, funding sources, and social and economic structures that have conferred power-including privilege, prominence, recognition, funding, opportunity, and decisionmaking authority-to institutions and individuals based in HICs.These deep-rooted structures have helped amplify the voices of those in HICs over the voices of those based in LMICs. 2 In such a system, it is accepted that HICs have expertise to provide and LMICs have capacity gaps to fill. 3 This imbalance is reflected in global health program planning, implementation, research, and publishing.4,5 We recognize that they are also reflected at GHSP.Amplified voice for those based in United States and elsewhere in HICs and diminished voice for those based in LMICs is a poor recipe for improving well-being or strengthening institutions around the world.6 Indeed, the notion that HICs have something to "teach" LMICs but nothing to learn is a reflection of skewed perceptions of expertise and power.These asymmetries have grown even more evident during the COVID-19 pandemic.7 Recent efforts to "decolonize global health" signal an increasing commitment by many players to address these issues of imbalance and inequity.8 At GHSP, we recognize that to meaningfully engage in addressing power imbalances, as a first step, we must look at our own attitudes and practices.We are especially interested in identifying how we need to do things differently to reflect a range of voices and perspectives in our journal that better corresponds to where this work is actually being done.9
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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 | 0.061 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.037 | 0.039 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".