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Record W4327968823 · doi:10.1017/hyp.2022.63

Finding Homeplace within Indigenous Literatures: Honoring the Genealogical Legacies of bell hooks and Lee Maracle

2023· article· en· W4327968823 on OpenAlexaff
Jennifer Brant

Bibliographic record

VenueHypatia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousPraxisSociologyGender studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract This article maps out a pedagogical juncture of bell hooks's feminist theory of homeplace (hooks 2007) and Indigenous maternal pedagogies as liberatory praxis through a journey with Indigenous women's literatures. I position this work as a response to the call to transform feminist theorizing through Indigenous philosophies as articulated in a recentHypatiaspecial issue (Bardwell-Jones and McLaren 2020, 2). The article documents hooks's theory of homeplace as a space of resistance and renewal and shares insights into Indigenous experiences of homeplace within historical and contemporary contexts of genocide, and the ongoing racialized and sexualized violence on Turtle Island. I discuss finding homeplace in Indigenous literatures by sharing a genealogy of Indigenous women's literatures as theorizing tools for engaging social change within academic spaces. To bring this work full circle, I offer Indigenous perspectives of homeplace, and the lessons gleaned from Indigenous women's literatures, as intentional work toward imagining Indigenous futurities. Indeed, connecting this work with liberatory pedagogical praxis imagines a site to establish homeplace in academic settings and empower students to engage in the kind of work that fosters and calls for safer homes, schools, and 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)0.006
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0170.057
Scholarly communication0.0090.010
Open science0.0010.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.360
Teacher spread0.320 · 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
Published2023
Admission routes1
Has abstractyes

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