Non-Indigenous Positionality when Engaging in de-colonising/ <i>re</i> -indigenising Research and Its Place Within Visual Methodology
Bibliographic record
Abstract
The aim and purpose of this article has been to advocate for autobiographical investigation and identification of positionality when non-indigenous researchers engage in indigenous methodologies. My experience as an international adoptee from a Romanian orphanage who experienced unsafe research practice shaped my emancipatory positioning and interpretive lens. It is from this positioning and experience of marginalisation I was concerned with finding a research paradigm that positions participants not as objective objects; but rather as rivers of knowledge with stories that determine the tide of the research. The lens of tangata tiriti (people of the treaty) from which I base my practice in Aotearoa, New Zealand; invites a positionality of neighbourliness, thus prioritising de-colonising/re-indigenising methodologies. The focus for my research project explored the disconnection between teachers who hold a Biblical world view and their students who experienced feelings of agitation and frustration. The participants contributed insights about teachers’ pedagogy drawn from responding to two prompts: “what is happening when you were empowered by your teachers to flourish?” and “what is happening when you feel disempowered by your teachers?” The chosen research method Photoyarn, is a recently emerged form of photo-elicitation created by Jessa Rogers that honours aboriginal yarning circles. This article makes the case for recognizing our ontological and epistemological positioning thus maintaining authenticity in research and holding steadfast the ethical disposition intention of doing no harm.
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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 | 0.092 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.085 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".