Applying <i>One Dish, One Spoon</i> as an Indigenous research methodology
Bibliographic record
Abstract
Conducting Indigenous research with a Western research methodology has barriers to achieving the maximum utility and benefit for the Indigenous community involved in the research project. This article discusses the translation of the author’s Haudenosaunee knowledge into a Western methodological framework of ontology, epistemology, axiology, and research methods to formulate Ogwehowehneha: a Haudenosaunee research methodology while also detailing its adaptation and application for use in an Anishnawbe context. I called this new adapted methodology One Dish, One Spoon, which references a covenant agreement between the Haudenosaunee and Anishnawbe to peacefully share lands and resources. By sharing my experience of researching as a Haudenosaunee scholar in an Anishnawbe context, I share my understanding of the need to advance commonalities of Indigenous law and philosophy while researching cross-culturally among Indigenous Nations.
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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 | 0.050 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.051 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".