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Record W4388406067 · doi:10.17953/a3.1554

Indigenous Methodologies of Care and Movement

2023· article· en· W4388406067 on OpenAlexaboutno aff
Michelle Daigle

Bibliographic record

VenueAmerican Indian Culture and Research Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousKinshipColonialismSociologyVisionMovement (music)Gender studiesTraditional knowledgeEnvironmental ethicsAnthropologyPolitical scienceEcologyLawAesthetics

Abstract

fetched live from OpenAlex

In this essay, I examine how research methodologies can draw from Indigenous peoples’ care work and mobilities to contribute towards Indigenous futurities. I draw on stories of my own research trajectory, that has been shaped by the support of Mushkegowuk women, and bring them into dialogue with Indigenous feminist theorizations of futurities, relationalities, care ethics and movement. I examine how methodologies of care can act as extensions of relations of care, and in the process, activate the complexities and expansiveness of Indigenous community, or what I call Indigenous relational geographies, through movement across lands and waters. I reflect on how Indigenous movement is learned and embodied through relations with the non-human world by grounding my discussion in the significance of water relations in the muskegs in so-called northern Ontario Canada and how they have helped me understand Mushkegowuk kinship relations as rippling out in and beyond that region. Overall, I am interested in how mobile relations of care evoke full and fluid conceptions of Indigenous kinship that exceed colonial spatialities, and end by considering how these relationships are crucial in shaping the visions and material relations of Indigenous and anti-colonial futurities moving forward.   

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)0.016
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.321
GPT teacher head0.611
Teacher spread0.290 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2023
Admission routes1
Has abstractyes

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