Ethical relationality, TribalCrit, and autobiographical narrative inquiry: Imagining coming alongside Indigenous children
Bibliographic record
Abstract
Creating this chapter brought us together as a diverse group of scholars to think deeply about a process of reflection in teacher education that centers on ethical relationality. To show our coming alongside adult learners attentive to reflection that centers ethical relationality, we inquire into both the Assessment as Pimosayta courses that Murphy, Cardinal, and Huber teach and into Stavrou's experiences teaching and enacting assessment in his practice. The body of our chapter is structured by the five design elements foregrounded by Stavrou and Murphy's recent bringing of critical race theory and anti-racist education to narrative inquiry: beginning with experience; carrying theoretical frameworks into an inquiry; negotiating theoretical frameworks with participants; using narrative threads to show the complexity of experience; ending in experience. Centering ethical relationality as we come alongside pre- and in-service teachers as they imagine coming alongside Indigenous children, youth, families, and communities lifts the long-termness of our work, including that this long-termness entails interactions and responsibilities with other humans and more-than-human beings.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".