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Record W2987228644 · doi:10.3167/ghs.2019.120308

Rekinning Our Kinscapes

2019· article· en· W2987228644 on OpenAlexaff
Sandrina de Finney, Shezell-Rae Sam, Chantal Adams, Keenan Andrew, K McLeod, Amber Lewis, Gabby Lewis, Michaela Louis, Pawa Haiyupis

Bibliographic record

VenueGirlhood Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousSovereigntySociologyDignityGenocideOppressionGender studiesStewardship (theology)Participatory action researchPoliticsTraditional knowledgeEnvironmental ethicsPolitical scienceCriminologyLawAnthropology

Abstract

fetched live from OpenAlex

“Sisters Rising” is an Indigenous-led research project that centers the gender knowledge of Indigenous youth and communities. In this article, members of “Sisters Rising” build on the notion of kinscapes to propose renegade stewardship as a generative concept through which to consider what kinds of responses are required at the community-scholarly-activist level to disrupt conditions of gender-based and sexual violence and racialized poverty that strip Indigenous bodies of sovereignty, land, and cultural connections while targeting us for genocide. Operating from a multimethod research standpoint that is land- and arts-based, community-rooted, and action-oriented, that engages youth of all genders, and that links body sovereignty to decolonization, this work seeks to build political, theoretical, ceremonial, and interpersonal channels that are crucial to restoring dignity with advocacy for and by Indigenous communities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0230.008
Scholarly communication0.0040.006
Open science0.0010.010
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0190.002

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 head0.035
GPT teacher head0.365
Teacher spread0.330 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2019
Admission routes1
Has abstractyes

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