Shapeshifting Power: Indigenous Teachings of Trickster Consciousness and Relational Accountability for Building Communities of Care
Bibliographic record
Abstract
Difficult dialogues are necessary work in order for communities to form coalitions, yet often these dialogues pose challenges for engaging in long-term work for social justice and systemic change. Power dynamics, microaggressions, and discomfort unlearning power and privilege can make long-term collaboration difficult. It is for this reason I discuss thinking of coalitions as communities of care and offer practical strategies for collaborating differently for sustainable action. Using Indigenous epistemology and methodology, Indigenous feminist and Indigequeer scholarship, as well as Indigenous land-based pedagogy and storytelling, I offer interventions using trickster teachings or trickster consciousness which I describe as comprised of a) humor and play to navigate discomfort, b) embracing multiplicity and the unknown, and c) embracing relationality as a site of meaning-making in critical and difficult dialogues. These foundations are rooted in Indigenous epistemology, center land/other-than-human-relationality, and employ Indigenous methodological and political frameworks of refusal by naming dialogic practices in organizations and higher education as Indigenous practices. As a Cree-Métis (Michel First Nation) author, I urge that dialogic practices seeking to employ trickster consciousness or other Indigenous frameworks not only cite Indigenous scholarship and intellectualism, but also radically shift hiring, acceptance, and/or inclusion practices to ensure Indigenous peoples are present and direct beneficiaries of the work in institutions and organizations.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.079 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".