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Intersectionality and reflexivity-decolonizing methodologies for the data science process.

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This summary is machine-generated.

This study reveals how intersectionality highlights data science flaws in analyzing the #metoo movement. Applying intersectionality fully can expose inequities and transform data science practices.

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Area of Science:

  • Social Sciences
  • Data Science Ethics
  • Digital Humanities

Background:

  • The #metoo movement's viral nature presented unique challenges for data science analysis.
  • Existing data science methodologies may overlook critical social dynamics and power structures.

Discussion:

  • Intersectionality as a methodology is crucial for uncovering hidden biases in data analysis.
  • Standard data science tasks can inadvertently perpetuate systemic inequities.
  • Data scientists must critically examine their role within power structures.

Key Insights:

  • Intersectionality exposes the limitations of conventional data science approaches when applied to social movements.
  • Analyzing the #metoo movement through an intersectional lens reveals significant nuances and inequities.
  • A reflexive approach is necessary for data scientists to understand their complicity in existing systems.

Outlook:

  • Future data science research must integrate intersectionality to ensure equitable and comprehensive analysis.
  • Developing new methodologies that embrace intersectionality can lead to more responsible data science.
  • Data scientists should actively work to decolonize their practices and challenge power structures.