Literature DB >> 33675042

The constraints of racialization: How classification and valuation hinder scientific research on human variation.

Tina Lasisi1.   

Abstract

Human biological variation has historically been studied through the lens of racialization. Despite a general shift away from the use of overt racial terminologies, the underlying racialized frameworks used to describe and understand human variation still remain. Even in relatively recent anthropological and biomedical work, we can observe clear manifestations of such racial thinking. This paper shows how classification and valuation are two specific processes which facilitate racialization and hinder attempts to move beyond such frameworks. The bias induced by classification distorts descriptions of phenotypic variation in a way that erroneously portrays European populations as more variable than others. Implicit valuation occurs in tandem with classification and produces narratives of superiority/inferiority for certain phenotypic variants without an objective biological basis. The bias of racialization is a persistent impediment stemming from the inheritance of scientific knowledge developed under explicitly racial paradigms. It is also an internalized cognitive distortion cultivated through socialization in a world where racialization is inescapable. Though undeniably challenging, this does not present an insurmountable barrier, and this bias can be mitigated through the critical evaluation of past work, the active inclusion of marginalized perspectives, and the direct confrontation of institutional structures enforcing racialized paradigms.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  hair; human phenotypic variation; pigmentation; race; skin

Year:  2021        PMID: 33675042     DOI: 10.1002/ajpa.24264

Source DB:  PubMed          Journal:  Am J Phys Anthropol        ISSN: 0002-9483            Impact factor:   2.868


  1 in total

1.  High-throughput phenotyping methods for quantifying hair fiber morphology.

Authors:  Tina Lasisi; Arslan A Zaidi; Timothy H Webster; Nicholas B Stephens; Kendall Routch; Nina G Jablonski; Mark D Shriver
Journal:  Sci Rep       Date:  2021-06-01       Impact factor: 4.379

  1 in total

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