Emotional experiences of non-Indigenous educators teaching Indigenous curricula: Reconciliation through narrative inquiry
Bibliographic record
Abstract
Non-Indigenous educators are required to teach Indigenous curricula according to Alberta Education (2018b) Teaching Quality Standards (TQS). This requirement hopes to contribute to the Truth and Reconciliation Commission of Canada (2015) (TRCC) calls to action regarding education in Canada. My research focuses on the calls to action which ask educators to teach Indigenous curricula with “intercultural understanding, empathy, and mutual respect” (TRCC, 2015). By asking the research question, what are the emotional experiences of non-Indigenous educators in Alberta who are teaching Indigenous curriculum, my purpose was to examine the emotional experiences as we work individually and collectively towards reconciliation in education. In this emotional journey that you are joining me on, the reader will encounter writing surrounding the Papal apology, situating myself in the research, situating the research in governing literature, approach to research and design, findings and discussion of the data. This thesis includes my design of Research in a Medicine Wheel which provides a conceptual and theoretical framework. In addition, the reader will encounter a Findings in a Medicine Wheel Word Cloud, and sweetgrass braid drawings which I created to show how my research is woven with research partners and the seven sacred teachings. Through a Narrative Inquiry approach, the findings present the stories of my three research partners and myself. In conducting an emotive analysis of these stories as data gathering, I created categories which represent themes found in each of the research partner’s stories. Susie presents the role of empathy in teaching Indigenous curricula; Elsi presents a story of growth, authenticity, and humility; and Quinn presents the perspective of moving past shock: a journey towards understanding. Each of the research partner’s stories are followed by my reaction and reflection which created the opportunity for me to become the fourth researcher in this research. This emotional journey includes a research process which became one of creating new relationships, the renewal of existing friendships, and changing relationships,. The research and writing which follows are centered on concepts found in the ever-changing processes of reconciliation.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| 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".