Land and storytelling: Indigenous pathways towards healing, spiritual regeneration, and resurgence
Bibliographic record
Abstract
This article aims to contribute to discourses of healing, Indigenous resurgence and spiritual regeneration within the context of the Indian Residential School Truth and Reconciliation Commission that took place in Canada between 2008 and 2015. First, it considers to what extent the TRC’s restorative justice process can relate to Indigenous ways of conceptualising healing. Secondly, it reflects on the Commission’s exclusive focus on the Indian Residential School system and its legacies, which, according to many Indigenous scholars, overlooks a much broader and more complex history of colonisation, political domination, and land dispossession still ongoing. I underline that, from an Indigenous perspective, land plays a fundamental role to achieve healing, spiritual regeneration, and resurgence. In the last section, I move the discussion to the literary dimension as I explore Richard Wagamese’s 2012 novel Indian Horse. In particular, I argue that fiction, especially that fiction produced during the years of the Commission’s work, can be a crucial site for challenging the TRC’s restorative process and for bringing out the significance of storytelling and of an Indigenous deep sense of connection to the land as a source of learning, spiritual reclaiming, and healing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.064 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".