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Record W4404623839 · doi:10.37119/ojs2024.v29i3.767

“Self” in Self-Study: Alongside Stories as Indigenously Understood Inquiry

2024· article· en· W4404623839 on OpenAlexaffvenue
Cher Hill, Awneet Sivia, Vicki Kelly, Paula Rosehart, Kauʻi Keliipio

Bibliographic record

Venuein education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of the Fraser ValleySimon Fraser University
Fundersnot available
KeywordsCognitive reframingIndigenousMeaning (existential)SociologyIdentity (music)PedagogyEpistemologyPsychologyAestheticsSocial psychology

Abstract

fetched live from OpenAlex

As part of our ethical responsibilities as scholar-practitioners and community members living as uninvited guests on Indigenous territories, we engaged in a collaborative inquiry to explore ways in which Indigenous pedagogies and worldviews extend understandings of self within self-study research. Over several years, we engaged in reflective conversations about our respective tensions, challenges, and successes in the effort to decolonize and Indigenize our pedagogies and research. These conversations moved us over time to a particular orientation as we shared our life stories as educators and women. We began by documenting our experiences and reflections at each meeting and shared in meaning making how our orientations shifted to ways of being in relation. The emerging synergies of our relationality led us to name our experiences “alongside stories,” in which we made meaning of the intersections and nuances between forms of self-study research and Indigenous Ways of Knowing. In sharing the alongside stories, we re-presented our collaborative understandings of inquiry as interweavings. These interweavings allowed us to explore how our knowledge and belief systems could be intertwined and disentwined to reveal resonances and particularities. Our exploration led us to reframe inquiry and self-study as Indigenously understood. Keywords: Indigenous, self-study, research, decolonizing, inquiry, alongside stories

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.120
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.380
Teacher spread0.336 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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