Evidence map›Paper›PMID 41854799›Full record

ArticleJournal of the history of biology2026

Of Epistemes and Insects: How Drosophila and Butterflies Shape Our Understanding of Radiation Risk.

Donna M Goldstein, Magdalena E Stawkowski

Abstract readHistorical Article
In one paragraph

Article in Journal of the history of biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Donna M GoldsteinUniversity of Colorado Boulder, Boulder, USA. donna.goldstein@colorado.edu.
Magdalena E StawkowskiUniversity of South Carolina, Columbia, USA. stawkows@mailbox.sc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper explores the complexities of extrapolating insect data to understand nuclear exposure effects on humans. Within radiation research, animal studies are invaluable tools for understanding biological effects of radiation exposure. However, data are often employed selectively, exposing unsettled science in extrapolating animal data to human radiation effects. Here we focus on our understanding of the genetic effects of radiation exposure, and how the debates about the long-term effects on humans were molded by research carried out on the model organism known as Drosophila; disagreements among scientists occurred within the constraints of research on this model organism, creating what we see as a possible Drosophila bias. By tracing how Drosophila became the dominant model organism for radiation genetics, we show how this choice created an epistemic framework that often dismissed contradictory evidence from other organisms, including the more recent Lepidoptera data from Fukushima. Even when animal studies demonstrate harm, they are often dismissed due to the belief-and some of the science-of human exceptionalism: that humans are intrinsically more resilient than animals. In this work, we consider Michel Foucault's ideas on epistemes as they apply to human exceptionalism in the context of radiation effects, mutation, and harm. By examining these two cases genealogically, we demonstrate how Drosophila's dominance established frameworks of population resilience that persist to this day, leading to the dismissal of contradictory butterfly findings as inconsistent with "conventional understanding" rather than as challenges to what counts as legitimate evidence in radiation biology.

Indexed as

ButterfliesDrosophilaRadiation ExposureAnimalsHistory, 20th CenturyHumans

Identifiers

PMID41854799
PMCPMC13216124

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.