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Related Experiment Video

Updated: Jul 6, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Clustering humans: on biological boundaries.

Ludovica Lorusso¹, Giovanni Boniolo

¹Department of Philosophy , via 8 Febbraio, 2-35122 Padova, Italy. ludovica.lorusso@gmail.com

Studies in History and Philosophy of Biological and Biomedical Sciences

|March 12, 2008

Summary

This study clarifies "boundary" and "cluster" concepts in genetics, finding they are property-dependent. Human clusters like race are shaped by how we analyze nature and properties.

Area of Science:

Genetics, including medical genetics, pharmacogenetics, and population genetics.

Background:

The concepts of

Purpose of the Study:

To clarify the under-discussed notions of

Main Methods:

Literature review to identify gaps in the discussion of

Main Results:

Clustering is fundamentally 'property laden,' relying on the selection of specific properties for analysis.
Three types of properties—main, investigating, and catalyzing—influence clustering decisions.
Human-created clusters, such as 'race' and 'ethnic group,' are products of our analytical frameworks.

Conclusions:

Boundaries and clusters among human populations are not inherent natural divisions but are constructed based on chosen properties.

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Published on: February 15, 2017

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Last Updated: Jul 6, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Understanding the property-laden nature of clustering is crucial for interpreting genetic and population data accurately.