Knowing Community through Story: It's Where We Come Together
Bibliographic record
Abstract
As PhD students and sessional lecturers, we undertook a collaborative narrative study* to explore our pedagogical and curricular approaches to decolonizing a community development course offered in our College of Education. We gathered our conversations, reflective journals, and notes, then wove together the narratives thematically using a métissage research methodology. We discovered ways we come together in the spaces in-between our different experiences, backgrounds, and worldviews, as Indigenous and non-Indigenous educators, decolonizing our curriculum and our students’ classroom experience. This paper shares one of the thematic braids we created, focused on the use of story for research, story as pedagogy and story for building relationships. We encourage educators to consider bridging their worldviews with other ways of seeing and knowing, to work toward decolonizing their teaching practices using story, and to form relationships across differences using story.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".