Radical Acts of Re-imaging Ethical Relationality and Trans-systemic Transformation
Bibliographic record
Abstract
This Indigenous métissage explores my engagement in Indigenous Arts-based Inquiry as a practice of Anishinaabe Ozihtoon or Indigenous making and knowledge generation. Anishinaabe Ozhitoon is a site that unlocks the theoretical potentialities of the intelligences within Indigenous Knowledge practices in contemporary contexts and reanimates Indigenous land-based assurgence. Reviving Indigenous artistic practices, as sites of co-imagining through constellations of co-creation, is part of ecological and community-based reconciliation and healing. Key to this process is the act of reciprocal recognition, a core practice that fosters ethical relationality, helps cultivate our Indigeneity, and honours the circle of life. This Indigenous métissage tracks the Indigenous pedagogical processes and Indigenous art making used in my own praxis and inquiry as a scholar while I worked in a university to create three pathways for trans-systemic knowledge creation: a university-wide President’s Dream Colloquium with an accompanying graduate course; a graduate diploma in Indigenous Education: Education for Reconciliation and a master’s in Indigenous Education: Truth, Reconciliation, and Indigenous Resurgence; and the Indigenous Research Institute initiation of an Indigenous Ethics Dialogue process as a trans-systemic pedagogical engagement with Indigenous and Western Knowledges, values, and ethics.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.086 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| 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".