Four Generations For Generations: A Pow Wow Story to Transform Academic Evaluation Criteria
Bibliographic record
Abstract
Within this article, I share a story of four generations of my family and community coming together through pow wow dancing. I present the storying and re-storing of Indigenous scholarly engagement through pow wow regalia making and dance to accomplish two things: 1) to center Indigenous knowledge, kinship and community work through scholarship; and 2) to generate merit and value in the good work in which Indigenous scholars engage. Our creative and cultural selves are often excluded in terms of what receives value and merit in collective agreements. The academy wants us to teach, publish, and engage in community service. My community service is often within Indigenous kinship and community service where I engage in creativity and expressive arts. Evaluations of our tenure attribute value, credit, and merit for work produced, service generated, and research conducted steeped in a eurowestern definition of scholarly work. We theorize about the significance and importance of our culture and traditions; however, our families and communities’ practices are regarded as external and outside of the eurowestern academic contexts. This article brings together the knowledge of preparing for and dancing in a pow wow as valued and good work of Indigenous scholars within the academy. It calls attention to a need to revise systems of value and merit in a manner that benefits Indigenous scholars’ whole knowledge systems.
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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 | 0.032 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.030 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.006 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".