Making Space in the Academy for the Curriculum of Belonging: Exploration of Indigenous Self, Coloniality and Relationships to Land
Bibliographic record
Abstract
What does it mean to reconcile our relationships with the Land, the assimilative and violent policies of the settler colonial project that is now Canada? How can we begin to understand our own responsibilities as we relate to this Land and all our relations? How do we untangle our familial curriculum of dislocation, as we unravel the stories of the wounds and bloodlines that make up our physical, social and spiritual DNA? Taking up these questions alongside Simpson’s (2014) wholistic definition of theory as “generated and regenerated continually through embodied practice and with each family, community and generation of people” (p. 7), I began my journey from the fractures of my familial wounds, the wounds of my Mi’kmaq ancestors and their ignored relationships to the Land in Ktaqmkuk (Newfoundland). Through journal entries, poetry and photographs, I documented my journey to familial sites in Ontario and Newfoundland with my teenage sons in the summer of 2019 as my own emerging curriculum of belonging. As educators, it is incumbent upon us to allow similar epistemic spaces for all learners to interrogate their own complex histories as they relate to the Land, coloniality and to the thousands-year-old relationships Indigenous peoples of North America have had with the Land. We must make spaces for them to invite the embodied, familial, personal stories to take form so that they can begin to enter their own curriculum of belonging (and responsibility) with this Land, wherever they are.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.052 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.012 |
| 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".