Because the machine can discriminate: How machine learning serves and transforms biological explanations of human...

Area of Science:

• Sociology of Science
• Neuroscience
• Quantitative Methods

Background:

• Scientific movements often focus on external resources like funding, overlooking internal methodological influences.
• Pinch's theory of technologies as institutions provides a framework to understand how methods can shape scientific movements.

Purpose of the Study:

• To argue that research methods can act as institutional resources for scientific movements.
• To demonstrate this by examining the adoption of machine learning in neuroscience and its impact on the sex difference movement.

Main Methods:

• Analysis of the adoption of machine learning in neuroscience.
• Examination of the "sex difference movement" and its reliance on specific quantitative methodologies.
• Application of Pinch's theory of technologies as institutions.

Main Results:

• Machine learning adoption in neuroscience offered a 'truly categorical' quantitative methodology.
• This methodology aligned with the sex difference movement's view of distinct male and female brains/bodies.
• The movement experienced a resurgence with increased publications and collaborations.

Conclusions:

• Research methods can institutionalize ideas and serve as crucial resources for scientific movements.
• Machine learning's influence may extend to other group-based scientific concepts like race and sexuality.
• Sociological attention to the internal workings of technologies is warranted due to their social consequences.