Stitching Together My Anishinaabe Research Paradigm: An Approach to Storytelling With Algonquin Ikwewag (Women) and Gender-Diverse People in Mattawa and North Bay, Ontario
Bibliographic record
Abstract
This article reviews an Anishinaabe research paradigm that structures a storytelling project with Algonquin Anishinaabekwewag and gender-diverse people from the Mattawa and North Bay area in Ontario, Canada. This Anishinaabe research paradigm contains an ontology, epistemology, methodology, and axiology that are informed by Anishinaabe worldviews and values, participants’ stories, as well as the knowledges and experiences the first author brings to the project as a mixed-ancestry Algonquin Anishinaabekwe. With a ribbon skirt framework of data analysis, analytical approaches from multiple knowledge systems are stitched together to form complex and cohesive stories. The methodology is guided by principles of ownership, control, access, and possession, the Tri-Council Policy Statement: Ethical Conduct of Research Involving Humans (Chapter 9), as well as Anishinaabe relational accountability which honours relationships with all of Creation. Axiology is further informed by Mino-Bimaadiziwin and Anishinaabe Original Instructions, and is expressed through Anishinaabe jiimaan (canoe) teachings and values of respect, relevance, reciprocity, responsibility, and reverence. Making space for Anishinaabe research paradigms that prioritize Anishinaabe ways of knowing and living is a powerful way to decolonize the research process and affirm the continuity of Anishinaabe lifeways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.042 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".