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Indigenous Statistics: Data Deficits to Sovereignty by Chris Andersen, et al.




Indigenous Statistics: Data Deficits to Sovereignty by Chris Andersen, et al. - Table of Contents

1. Introduction - Maggie Walter, Chris Andersen, Tahu Kukutai and Chelsea Gabel 2. A Decade of Data Revolutions: Big Data and Indigenous Data Sovereignty - Maggie Walter, Chris Andersen and Tahu Kukutai 3. The Statistical Field, Writ Indigenous - Chris Andersen 4. Statistics and the Neo-Colonial Alliance: “Seeing” the Indigene - Maggie Walter 5. Beyond Colonial Constructs: The Promise of Indigenous Statistics - Tahu Kukutai 6. Statistics, Stigmatization and Stereotyping: The Importance of Authentic Partnering and Community Engagement to Validate Indigenous Statistical Research - Chelsea Gabel 7. Métis Population Data in Canada: A Conceptual Case Study - Chris Andersen and Chelsea Gabel 8. “Fixing” the Figures: Tribal Data in the Aotearoa New Zealand 2018 Census - Tahu Kukutai 9. Doing Indigenous Statistics in Australia: The Racial Burden of Disregard - Maggie Walter

What You Will Learn in Indigenous Statistics: Data Deficits to Sovereignty by Chris Andersen, et al.

"Indigenous Statistics: From Data Deficits to Sovereignty" by Chris Andersen, Maggie Walter, Tahu Kukutai, Chelsea Gabel is a critical and innovative book that examines how traditional statistics often fail to represent Indigenous peoples accurately. It discusses the problem of "data deficits", where Indigenous communities are undercounted or portrayed only through negative narratives. The book argues that mainstream statistical systems can reflect colonial assumptions, which may distort understanding of "Indigenous communities" and their realities. Instead, it promotes approaches that respect cultural contexts and empower communities in the data process. A major concept in the book is "data sovereignty", which means Indigenous peoples should control how their data is collected, used, and interpreted. This approach ensures that statistics support self-determination and meaningful decision-making rather than external control. The authors, including Chris Andersen, provide examples from different countries to show how "quantitative research" can be redesigned to respect Indigenous perspectives. The book connects statistical practice with ethical responsibility and community engagement. Overall, this text challenges conventional ideas about "statistical methods" and encourages more equitable ways of working with data. It is valuable for researchers, policymakers, and students who want to understand the social impact of numbers and data interpretation. By emphasizing "Indigenous statistics" and sovereignty, the book contributes to a more inclusive and respectful approach to knowledge production and evidence-based decision making.

Book Details & Specifications

Title: Indigenous Statistics: Data Deficits to Sovereignty by Chris Andersen, et al.
Publisher: Routledge
Year: 2025
Pages: 175
Type: PDF
Language: English
ISBN-10 #: B0DHV65M1D
ISBN-13 #: 978-1040298985
License: CC BY-NC-ND 4.0
Amazon: Amazon

About the Author: Chris Andersen, Maggie Walter, Tahu Kukutai, Chelsea Gabel

The author Chris Andersen, Maggie Walter, Tahu Kukutai, Chelsea Gabel "Chris Andersen" is a Métis scholar from Saskatchewan, Canada, and a professor at the "University of Alberta". He earned his PhD in sociology and specializes in "Indigenous studies", identity, and data sovereignty. "Maggie Walter", a leading Indigenous sociologist from Australia. She works at the "University of Tasmania" and focuses on Indigenous data, social inequality, and representation in statistics. Another contributor is "Tahu Kukutai", a prominent researcher in population statistics and Indigenous data sovereignty. She works at the "University of Waikato", studying how data shapes Indigenous identities and policy. Together, the authors emphasize ethical data use and statistical approaches that respect Indigenous perspectives and self-determination.

Read or Downloadable Indigenous Statistics: Data Deficits to Sovereignty by Chris Andersen, et al.

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