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Enregistrement W4362510481 · doi:10.1353/nai.2023.0033

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

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

Notice bibliographique

RevueNative American and Indigenous Studies · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueIndigenous Studies and Ecology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIndigenousSovereigntyColonialismSociologyNarrativePolitical scienceLawPoliticsArt

Résumé

récupéré en direct d'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...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,309
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0080,001
Communication savante0,0000,000
Science ouverte0,0000,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,091
Tête enseignante GPT0,474
Écart entre enseignants0,382 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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