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 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".