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Updated: Jun 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Strategic aggregation: A more equitable approach to creating two-category variables.

Jennifer Lane^1,2,3, Megan White⁴, Holly McCulloch⁵

¹Faculty of Health, Dalhousie University, Halifax, NS, Canada. jennifer.lane@dal.ca.

Canadian Journal of Public Health = Revue Canadienne De Sante Publique

|June 3, 2026

Summary

Government data aggregation using two-category variables can erase intersex people and non-binary identities. A more equitable approach is needed to represent sex and gender differences without causing harm.

Keywords:

Data aggregation Gender Intersectionality Misclassification bias Sex Strategic aggregation Strategic essentialism

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Area of Science:

Social Sciences
Demographics
Gender Studies

Background:

Two-category data aggregation simplifies comparisons but struggles with complex social constructs.
Current methods risk compromising anonymity and erasing marginalized group experiences.

Purpose of the Study:

Critique the Government of Canada's two-variable data approaches, including the 2021 Census.
Highlight the erasure of intersex people and non-binary identities in binary data.
Propose a more equitable data aggregation strategy.

Main Methods:

Analysis of Government of Canada's data aggregation practices.
Critique of binary variable application to sex and gender.
Exploration of strategic essentialism for equitable data representation.

Main Results:

Two-category variables in Canadian census data erase intersex individuals and collapse non-binary identities.
Current practices may perpetuate historical injustices despite efforts to include gender-diverse people.

Conclusions:

Binary data aggregation can obscure and erase diverse sex and gender experiences.
Strategic essentialism offers a framework for understanding differences equitably.
Transparency and community consultation are crucial for ethical data practices.