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Record W4362510481 · doi:10.1353/nai.2023.0033

Indigenous Data Sovereignty and Policy ed. by Maggie Walter et al.

2023· article· en· W4362510481 on OpenAlexaboutno aff
Jeffrey D. Burnette

Bibliographic record

VenueNative American and Indigenous Studies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSovereigntyColonialismSociologyNarrativePolitical scienceLawPoliticsArt

Abstract

fetched live from OpenAlex

Reviewed by: Indigenous Data Sovereignty and Policy ed. by Maggie Walter et al. Jeffrey D. Burnette (bio) Indigenous Data Sovereignty and Policy edited by Maggie Walter, Tahu Kukutai, Stephanie Russo Carroll, and Desi Rodriguez-Lonebear Taylor & Francis, 2021 statistics have long been used as a tool for shaping the narrative about Indigenous People, communities, and nations through the use of 5 D data—“a set of items related almost exclusively to measure Indigenous difference, disparity, disadvantage, dysfunction and deprivation” (9). This pathologizing approach to data creation and analysis has led to dysfunctional policies that are then used to justify the need for more data focused on the 5 Ds and that shape dominant society’s understanding of Indigenous People. Indigenous Data Sovereignty and Policy drives home the problematic nature of this approach to data collection and analysis through the use of case studies while centering the discussion on Indigenous data sovereignty (IDS) and Indigenous data governance (IGOV). The editors have assembled a wide array of examples that demonstrate the many ways that data has developed dominant society’s incomplete and inaccurate understanding of Indigenous People, communities, and nations. At the same time, the anthology enables readers to connect common themes that are consistently applied to national datasets across countries. Chapter 1 by Walter and Carroll establishes the strong foundation between IDS, IGOV, and government policy that makes the edited volume’s approach work by defining IDS and discussing how colonial states use data to construct narratives of difference. Walter and Carroll detail how IDS and IGOV combat current dominant narratives and describes current national IDS networks. Chapter 15 by Walter, Carroll, Kukutai, and Rodriguez-Lone-bear usefully closes the topic by reflecting on the challenges and opportunities facing the IDS movement and summarizing earlier chapters. Each chapter in between these bookends provides a case study that exemplifies either: (1) IDS in a specific area; (2) how data is used to create contested narratives; or (3) how Indigenous nations are succeeding in creating and governing their own data. For example, chapters 2, 3, 5, and 6 describe and analyze IDS in Aotearoa New Zealand, Australia, and Canada, while other chapters extend the analysis to include Colombia, Mexico, Spain, Sweden, and the United States. Chapters 4 and 10 detail successful examples of Pueblo and Quechan data sovereignty in practice. More importantly, [End Page 144] they also demonstrate the key role that values and culture play in shaping what data is created, discussing their process and describing lessons learned. In chapter 7, Bengoetxea examines how the lack of agency and fear concerning the misuse of ethnicity data has rendered Sami People invisible in national population statistics. Chapters 11, 13, and 14 connect IDS to other areas or disciplines. The ways universities and Institutional Review Boards can support IDS are presented in chapter 11, while chapter 14 focuses on its legal dimensions. Chapter 13 by Paine, Cormack, Reid, Harris, and Robson demonstrates how the choice of statistical technique and framing privilege non-Indigenous communities. For instance, certain statistics like morbidity rates are standardized to allow comparisons across different groups. For morbidity rates, the age structure of non-Indigenous populations is used for standardization, ensuring morbidity rates more accurately reflect non-Indigenous experiences. A constant theme throughout the anthology revolves around Indigenous identity: Who gets to define it and how it is operationalized for data collection? Chapters 8, 9, and 12 explicitly explore the importance of the answers to these questions in Basque Country, Mexico, and Colombia. In the case of Basque Country in Spain, the question of Indigeneity is framed against that of a minority population, while exploring the power of data to shape public perception. Meanwhile, chapters 9 and 12 focus on ways of defining “Indigenous.” Mexico uses physical features, culture, and the sense of community to define individuals as Indigenous, whereas Colombia’s definitions originate from transitional justice tribunal rulings. Another valuable element of the collection is the repeated demonstration that the mining of Indigenous data by non-Indigenous nations is just the most recent example of colonial powers extracting resources from Indigenous People, communities, and nations. Making this connection helps detach the common misperception that data merely demonstrate objective facts and...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.474
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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