Non‐Indigenous Canadians’ Attitudes Toward Renaming or Removing Statues as a Reconciliation Strategy
Bibliographic record
Abstract
ABSTRACT Reconciliation with Indigenous Peoples has been a named priority for many post‐colonial societies. In this context, in August 2018, Victoria City Hall in Canada removed the statue of Sir John A. Macdonald, Canada's first Prime Minister, from its grounds; similar events followed across Canada. Research on this issue is lacking but can offer useful insights to researchers and policymakers. To understand how non‐Indigenous Canadians respond to renaming or removing statues in the name of reconciliation, we qualitatively analysed online comments posted under news articles reporting the removal of Macdonald's statue (Study 1). Two narratives aimed at delegitimising renaming/removing emerged: depicting the actions as excessive ‘political correctness’ (PC) that represented the values of a powerful, but minority, outgroup of ‘liberal elites’; and depicting the actions as a symbolic threat to the ingroup through notions of ‘rewriting history’. In Study 2, with a Canadian community sample, we investigated anti‐PC attitudes and symbolic intergroup threat via rewriting history as predictors of support for reconciliation with Indigenous Peoples. Given the central role of ideological beliefs in intergroup attitudes, we examined RWA and SDO as predictors of anti‐PC attitudes, symbolic threat in the form of rewriting history, and support for reconciliation. Path analysis results showed that RWA and SDO indirectly predicted lower support for renaming/removing via higher anti‐PC attitudes and higher symbolic threat. Collectively, this research provides evidence that anti‐PC and symbolic threat are important constructs in relation to responses to reconciliation proposals in Canada with potential implications for other post‐colonial societies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".