ANI-ANISHINAABEWAADIZING (Becoming Indigenous): Healing from Sustained Cultural Genocide and Linguicide through Ojibwe Language World View
Bibliographic record
Abstract
Intergenerational transmission of culture and language across Native American communities in the United States has been obstructed by more than 520 years of sustained cultural genocide and attempts of linguicide which, in turn, inflicted intergenerational trauma that still impacts Native American communities today. This article intentionally bridges understandings of language revitalization efforts in a North American Ojibwe community employing Indigenous research methods. The community efforts are reflected upon by an Ojibwe Elder and longtime language teacher, which is then responded to by two colleagues and friends of the Elder who come from a Western perspective. This “two-eyed” approach illustrates the importance of Indigenization in education and communities as a process of truth, reconciliation, and healing after genocide. Once the historical context is given, the Elder's reflections of language revitalization are understood through mapping the Medicine Wheel and as having mental, spiritual, emotional, and physical impacts. Further, the work of the community to reclaim and recenter the schools and community to the Ojibwe worldview is viewed as weaving together the Seven Grandfather Teachings of Love, Respect, Honesty, Truth, Wisdom, Humility, and Bravery as acts of resistance to continued actions of the dominant US culture that degrade and dishonor Native American nations and communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".