Digital Narratives as a Means of Shifting Settler-Teacher Horizons toward Reconciliation.
Bibliographic record
Abstract
The Truth and Reconciliation Commission’s Calls to Action report (2015), in the section “Education for Reconciliation” (p. 7, #62–63), calls for the integration of Indigenous knowledge and teaching methods into the curriculum and for better preparation of teachers to deliver Indigenous content. Settler-teachers, however, have not been adequately prepared for this mission, nor are they well-prepared to teach Indigenous students. This article discusses a project of dual purpose in support of reconciliation: to give Indigenous students the opportunity to represent their Land-based dogsledding experiences as iMovie digital narratives and to give settler-teacher candidates direct experience for relationship-building in an indigenized context of education. Drawing upon theories of settler-colonialism, decolonization, and reconciliation in education, the article illustrates the imperative of immersing settler-teachers into contexts where Indigenous students self-representing their identities and Indigenous knowledge are at the centre of the curriculum.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".