Stories of Reconcili-Action through Praxis-based Learning Opportunities
Bibliographic record
Abstract
As teacher educators engaged in the work of reconciliation, we believe it is necessary to engage in praxis-based learning opportunities that move towards reconciliation. Drawing on the service-learning experiences of graduates of the Indigenous education: A call to action program at the Werklund School of Education, our session shares digitalstories of reconciliation as experienced by our students. These stories move beyond abstract conceptualizations of what reconciliation could look like to the realm of action based Reconcili-Action (Anishinaabe Elder, Commanda 2017). Our session provides a glimpse into some of the ways in which our students have enacted reconciliatory projects, and the insights they have gained along the way.By exploring these student stories of hope, and heartbreak, through a combined methodology of phenomenology with that of storytelling, we are able to intertwine Indigenous and non-Indigenous perspectivesin a creative research approach known as Metissage (Donald, 2012). Through a process of reconciliatory learning, we have witnessed our students assert the need for social change through a redistribution of power and the building of authentic relationships (Cipolle, 2010; Mitchell, 2007; Author & Author, 2017). Our session shares powerful stories of reconciliatory learning with others invested in moving towards 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".