Relational Narrative Inquiry Alongside a Young Métis Child and Her Family: Everyday Assessment Making, Pimatisiwin, Pimosayta, and Teacher Education and Development
Bibliographic record
Abstract
Understandings of diverse children, families, and communities/peoples as holding knowledge of and as practicing assessment is little recognized in research for, or in programs of, teacher education and development. Our paper shows the intergenerational relational living that Suzy, a young Métis child, experienced alongside her family as they imagined forward and remembered backward. This process shaped, and was shaped by, the family and Suzy’s continuous assessment of her ongoing making of a healthy life. We see important connections between Suzy’s and her family’s everyday assessment making practices and our experiences alongside Anishinaabe kwe scholar Mary Isabelle Young (Singing Turtle Woman), who lived with usPimatisiwin(walking in a good way) andPimosayta(learning to walk together). Dominant narratives of accountability in universities and schools most commonly serve the institution or government. Much potential opens in teacher education and development when we shift from these orientations to orientations that lift the particularities of each person and our collective responsibilities to all our relations. In this way we move closer to fulfilling our responsibilities to the people and worlds around us, to all of creation, the animals, plants, Earth, and cosmos, and to the next generations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".