Explorations into narrative assessment and Indigenous ways of knowing
Bibliographic record
Abstract
Using an Indigenous research methodology, this research examines how educators in British Columbia use narrative assessment as a more inclusive, strength-based approach to assess learners and intends to uncover teacher perceptions on the connections between narrative assessment and the First Peoples’ Principles of Learning / Indigenous ways of knowing. As a self-identified Métis researcher, my intention is that this study will contribute to a growing body of research that is framed using an Indigenous methodology and recognizes the significance of co-creating knowledge through reciprocal relationships. This research utilized collaborative methods with educators sharing alongside a Kwakwaka’wakw Elder in the form of a ‘talking circle.’ Qualitative data was collected on participant perceptions of their personal experiences using narrative assessment in the classroom and its connection to Indigenous ways of knowing. The main themes that emerged from the data were: Responsibility and Action, Relevance and Well-being, Reciprocity and Being-together, and Respect and Stories. While each distinct, these four themes are interconnected and represent the relational nature of Indigenous knowledge which includes community, family, students, and self at its core. Central findings of this study suggested that a narrative assessment approach enables educators to shift their practice toward a more inclusive and joyful process which invites reciprocity, relationship building, and co-learning opportunities.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".