An Indigenous Research Methodology That Employs Anishinaabek Elders, Language Speakers and Women’s Knowledge for Sustainable Water Governance
Bibliographic record
Abstract
Indigenous research paradigms are congruent to Indigenous worldviews and have become more dominant in areas such as Indigenous policy and education. As Indigenous research paradigms continue to gain momentum, the historical legacy of unethical research is addressed as more Indigenous communities and organizations develop their own research protocols. There is a plethora of articles explaining Indigenous research methodologies, but few examine the inclusion of the knowledge from Elders, language speakers, and Indigenous women in sustainable water governance. My Indigenous research methodology draws on the works of Indigenous scholars Shawn Wilson, Linda Smith, and Margaret Kovach, with specific focus on Wendy Geniusz’s Biskaabiiyang. My Indigenous research methodology is specific to the Anishinaabe territory of the Great Lakes region and includes Anishinaabek Elders, Anishinaabemowin (Ojibway language) speakers, and Anishinaabek women. This article seeks to contribute to Indigenous research paradigms and methods by elucidating the importance of engaging Anishinaabek Elders, Anishinaabemowin speakers, and Anishinaabek women in sustainable water governance.
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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 | 0.033 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".