Unsettling public history: a promising methodology for settler decolonization
Bibliographic record
Abstract
Facilitating settler decolonization is multi-faceted and non-linear. Based on existing scholarship, the key components that facilitate settler decolonization encompass taking responsibility for one’s own learning and unlearning, self-examining that leads to a decolonial practice, building relationships with Indigenous people and place, and revising settler narratives to acknowledge the settler problem and Indigenous sovereignty. While this theoretical framework provides principles for settler decolonization, there is a research gap in applying settler decolonization theory in particular places and cases. Employing theories of whiteness in this framework, I explore an approach for settler decolonization through re-storying public history. To enrich the methodology, I utilize a case study of early Indigenous-Mennonite relations in what is now the Waterloo Region. I argue that through the braiding of Indigenous and Western historiography, a decolonial public history would centre Indigenous sovereignty, recognize the settler problem, acknowledge complicity, deepen relationships with place, renew treaty relationships, disrupt affect and embrace uncertainty, and foster settler responsibility.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".