Narrative inquiry: locating Aboriginal epistemology in a relational methodology
Bibliographic record
Abstract
BACKGROUND: This methodology utilizes narrative analysis and the elicitation of life stories as understood through dimensions of interaction, continuity, and situation. It is congruent with Aboriginal epistemology formulated by oral narratives through representation, connection, storytelling and art. Needed for culturally competent scholarship is an experience of research whereby inquiry into epiphanies, ritual, routines, metaphors and everyday experience creates a process of reflexive thinking for multiple ways of knowing. Based on the sharing of perspectives, narrative inquiry allows for experimentation into creating new forms of knowledge by contextualizing diabetes from the experience of a researcher overlapped with experiences of participants--a reflective practice in itself. AIM: The aim of this paper is to present narrative inquiry as a relational methodology and to analyse critically its appropriateness as an innovative research approach for exploring Aboriginal people's experience living with diabetes. NURSING APPLICATION: Narrative inquiry represents an alternative culture of research for nursing science to generate understanding and explanation of Aboriginal people's 'diabetic self' stories, and to coax open a window for co-constructing a narrative about diabetes as a chronic illness. The ability to adapt a methodology for use in a cultural context, preserve the perspectives of Aboriginal peoples, maintain the holistic nature of social problems, and value co-participation in respectful ways are strengths of an inquiry partial to a responsive and embodied scholarship.
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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 | 0.038 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".