Using Indigenous Research Frameworks in the Multiple Contexts of Research, Teaching, Mentoring, and Leading
Bibliographic record
Abstract
Indigenous research frameworks can be used to effectively engage Indigenous communities and students in Western modern science through transparent and respectful communication. Currently, much of the academic research taking place within Indigenous communities marginalizes Indigenous Knowledge, does not promote long-term accountability to Indigenous communities and their relations, and withholds respect for the spiritual values that many Indigenous communities embrace. Indigenous research frameworks address these concerns within the academic research process by promoting values such as: relationality, multilogicality, and the centralization of Indigenous perspectives. Indigenous research frameworks provide a framework that can be used in multiple contexts within higher education to bring equitable practices to research, teaching, mentoring, and organizational leadership. In this article, as a researcher who uses Indigenous research frameworks, I utilize autoethnography to engage in critical, reflexive thinking about how my perspective as an Indigenous researcher has developed over time. The purpose of this autoethnography is to reveal how Indigenous research frameworks may enhance higher education, especially for Indigenous students.
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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 | 0.079 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.021 | 0.066 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.017 |
| 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".