“Self” in Self-Study: Alongside Stories as Indigenously Understood Inquiry
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.073 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".