Postmodern anti-science attitudes?

2026-04-22 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Life Sciences & Biology, Social Sciences & Behavioral Studies, Research Methodology & Innovation · Depth: Advanced, short

Summary

John Williams observes an increasing presence of postmodernist views in the magazine Science, particularly noting a set of opinion pieces in the February 23, 2024, issue concerning genetics education. These pieces, exemplified by Duncan et al., argue that genetics research is deeply influenced by societal power dynamics, leading to biased questions and population definitions, historically supporting eugenics and continuing to inform racist logic. Williams acknowledges that science is conducted by people and thus subject to human biases, but finds the postmodernist claims in Duncan et al.'s essay to be overblown and not entirely well-founded, citing a reference that attributes geographical genetic clusters to sampling strategy. He expresses concern that while acknowledging science's value, postmodernism often seeks to de-privilege scientific knowledge, and criticizes Duncan et al. for not addressing how genetics itself has undermined concepts like biological race.

Key takeaway

For genetics educators and researchers grappling with the historical misuse of scientific findings, you should prioritize teaching core genetic principles thoroughly. Demonstrating how genetic evidence, such as Lewontin's 1972 findings on within-group diversity, refutes biological race concepts is more effective than simply asserting their invalidity, fostering critical understanding over prescriptive statements.

Key insights

Postmodernist views in science argue that research is inherently shaped by societal power dynamics and dominant cultures.

Principles

• Scientific knowledge is socially constructed.
• Genetics research has historically supported eugenics.
• Sampling strategy can influence perceived genetic clusters.

Topics

• Postmodernism
• Genetics Education
• Scientific Objectivity
• Eugenics History
• Race and Genetics

Best for: Research Scientist, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.