Reading (Re)conciliation in White Settler and Chinese Canadian Narratives: From Liberal toward Transformative Approaches
Bibliographic record
Abstract
This article explores how four settler narratives situate themselves differently within the reconciliation discourse in response to the Final Report of the Truth and Reconciliation Commission of Canada. In my reading of Gail Anderson-Dargatz’s The Spawning Grounds (2016) and Jennifer Manuel’s The Heaviness of Things That Float (2016) alongside Doretta Lau’s “How Does a Single Blade of Grass Thank the Sun?” (2014) and Amy Fung’s Before I Was a Critic I Was a Human Being (2019), I show how these narratives express different degrees of critical reflection on the settler colonial state and differ in their acknowledgement of Indigenous resurgence. I adopt David B. MacDonald’s distinction between “liberal reconciliation,” which is based on a “shared vison of a harmonious future,” and “transformative reconciliation,” which “is about fundamentally problematizing the settler state as a colonial creation, a vector of cultural genocide, and one that continues inexorably to suppress Indigenous collective aspirations for self-determination and sovereignty” as a critical framework.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.050 | 0.054 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".