Walking Alongside: Poetic Inquiry into Allies of Indigenous Peoples in Canada
Bibliographic record
Abstract
This qualitative arts-based study made use of poetic inquiry to analyze and represent the stories of non-Indigenous people recognized as allies of Indigenous peoples in Canada. I adopted a theoretical foundation in critical realism, focusing on the role of agency in the emergent realities of the participants’ ally work (Archer, 2002). I grounded the study in literatures that drew from multiple Indigenous perspectives on teaching, learning and knowledge; social justice education and awareness; and postcolonial theory and decolonization. Thematically, the areas of ally experience that interested me most were their actions, emotions, and how they related to others in the spaces they occupied. Using the ally interview transcripts as raw data, I created found poems that reflected those themes. Constructing the poems while engaging in analysis led me to attempt to decolonize language and names. Hence, I made use of a disruptive strategy to bring attention to the extent to which language reflects colonization. In the final chapter of the dissertation, I outlined implications for adult education theory and practice as suggested by the study. In addition, I made suggestions for actions that allies-in-the-making may take up and directions for future study.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.052 | 0.036 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".