Increasing Support for Reconciliation in Settler Colonial Societies
Bibliographic record
Abstract
Profound inequalities between Indigenous and non-Indigenous individuals persist to this day in settler colonial societies worldwide. Reconciliation processes require collective acknowledgment of past harm and reparations to redress it. However, this process may be challenged by widespread misapprehensions about, and animus toward, Indigenous populations. But what happens when citizens are confronted with information about the prevalence and extent of the ongoing consequences of settler colonialism? Leveraging the case of Canada, we argue raising awareness about structural inequalities should reduce misinformation and prejudice, ultimately helping make progress toward reconciliation. We field a national survey of 3,000 non-Indigenous Canadians to test two distinct informational interventions commonly used in the misinformation and prejudice reduction literatures: fact-checking and perspective-getting narratives. Both interventions prove successful, leading to increased recognition of inequality and support for policy reforms, reduced anti-Indigenous resentment, and higher rates of advocative behavior in the form of an anonymous note addressed to the House of Commons of Canada. Fact-checking more effectively motivates awareness and advocacy, whereas narratives boost support for actual policy change the most. Surprisingly, we observe diminishing effects of combining both interventions. These findings pave a way forward for advocates of reparations by identifying promising strategies to rally mass public support.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".