Embodied in Indigenous Research: How Indigeneity, Positionality, and Relationality Contribute to Research Approaches and Understanding
Bibliographic record
Abstract
Objectives: As the presence of Indigenous Peoples, world views, perspectives, and teachings continues to grow within academia, the institutional narrative regarding Indigenous approaches to knowing, doing, and being evolves and expands. We would like to contribute to this shifting narrative. Introduction: We are a diverse group of trainees invited into an Indigenous-led research project, entitled IndWisdom, that is exploring the context-mechanism-outcome relationships of Indigenous research. By conducting two parallel study components—an Indigenous-informed realist review and case studies—the larger IndWisdom project aims to advance Indigenous Peoples’ sovereignty and rights related to how Indigenous Knowledges are centred in research. Through the process of this research, we have come to the understanding that Indigenous Knowledges and Indigenous Knowledge Systems are contextualized and dynamic in nature and are embodied and interconnected in all aspects of one’s lived experience, language, traditions, and culture. Methods: As a collective, the trainees were supported to participate in a sharing circle to introduce ourselves and reflect on how our positionality and understanding of who we are impacts our approach to engaging with research. Results: While we span different nationhoods and time zones, we share how we have fostered virtual spaces that respect each other's perspectives and approaches as well as honour our own Indigenous world views and allied identities. Discussion: In the same way that our realist review involves recording and analyzing context-mechanism-outcome details of other peoples’ studies, our paper provides the context of who we are as co-authors, our mechanisms (approaches) of engaging with each other and the IndWisdom study content, and outcomes from our ways of knowing and doing research.Keywords: Indigenous, research, typology/methodology, lived experience, Indigenous research methodologies, ways of knowing
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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 | 0.099 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.143 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".