Related Experiment Video
Updated: Oct 24, 2025

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Epistemic injustice in academic global health
Himani Bhakuni1, Seye Abimbola2
1Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands; Department of Foundations and Methods of Law, Maastricht University, Maastricht, Netherlands.
Abstract:
This Viewpoint calls attention to the pervasive wrongs related to knowledge production, use, and circulation in global health, many of which are taken for granted. We argue that common practices in academic global health (eg, authorship practices, research partnerships, academic writing, editorial practices, sensemaking practices, and the choice of audience or research framing, questions, and methods) are peppered with epistemic wrongs that lead to or exacerbate epistemic injustice. We describe two forms of epistemic wrongs, credibility deficit and interpretive marginalisation, which stem from structural exclusion of marginalised producers and recipients of knowledge. We then illustrate these forms of epistemic wrongs using examples of common practices in academic global health, and show how these wrongs are linked to the pose (or positionality) and the gaze (or audience) of producers of knowledge. The epistemic injustice framework shown in this Viewpoint can help to surface, detect, communicate, make sense of, avoid, and potentially undo unfair knowledge practices in global health that are inflicted upon people in their capacity as knowers, and as producers and recipients of knowledge, owing to structural prejudices in the processes involved in knowledge production, use, and circulation in global health.
More Related Videos
09:55Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
Published on: September 28, 2022
05:47Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Related Concept Videos
Bias in Epidemiological Studies
Fundamental Attribution Error
Causality in Epidemiology
Ethics in Research
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...