Nourishing the Re/Conciliatory Spirit in Teacher Education: Settlers
Bibliographic record
Abstract
This duoethnographic study (Norris 2008) responds directly to the Truth and Reconciliation Commission Calls to action on reconciliation education by exploring the possibilities and pitfalls of settler engagement in reconciliation education. Situated in an Indigenous-decolonizing paradigm (Smith, 1999) we employed Kovach's (2011) conversation-as-method to document our duo-ethnographic inquiry of our experiences as settler-teacher educators striving to foster meaningful reconciliation education pedagogies and practices for ourselves and amongst other educators. Data for the study consisted of email text and recordings of phone and skype conversations that took place between the co-authors over a one-year period. Following a qualitative thematic analysis of the data, our findings and interpretation explore complexities of implementing reconciliation education in/through settler teacher education of pre-service and in-service teachers. These included, settler decolonizing learning processes towards respectful relationality; engagement with Indigeneity including with Indigenous peoples, their cultures and knowledges and our shared histories, and with Indigenous Land and all beings; engagement with settler culture and identity; and understandings of settler-colonialism past and present including dimensions of power of power, privilege, inequity and injustice.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".