Movements Toward Living Relationally Ethical Assessment Making: Bringing Indigenous Ways of Being, Knowing, and Doing Alongside Narrative Inquiry as Pedagogy
Bibliographic record
Abstract
As teacher educators deeply committed to relational narrative inquiry and the centrality of living in relationally ethical ways alongside co-researchers, our initial turns toward living narrative inquiry as pedagogy were inspired by wanting to live in relationally ethical ways alongside undergraduate and graduate students. Following the sudden passing in 2015 of Singing Turtle Woman—Anishinabe kweElder, scholar, and long-time friend and research collaborator Mary Isabelle Young, we often told and retold stories of how her teachings of Pimosayta (learning to walk together) and Pimatisiwin (walking in a good way) were continuing to guide us. In this midst we gradually realized that Mary’s teachings opened potential in conjunction with our desires to live/practice relationally ethical assessment making alongside students. As we engage in autobiographical narrative inquiry into our recent coming alongside undergraduate and graduate students, in two Assessment as Pimosayta courses in two differing teacher education programs in Canada, we show how our bringing Indigenous ways of being, knowing, and doing alongside our practicing narrative inquiry as pedagogy supported our movements toward living relationally ethical assessment making.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.064 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".