The Canadian Reconciliation Barometer: a rigorous tool for tracking reconciliation in Canada
Bibliographic record
Abstract
Indigenous peoples in Canada have resisted centuries of colonial harm. In response to their resurgence and calls for justice, Canada is now on what is likely to be a long and winding truth and reconciliation journey. To help monitor perceptions of reconciliation progress in a good way, our team of Indigenous and non-Indigenous researchers created the Canadian Reconciliation Barometer. In Study 1, we wrote 89 self-report items representing 13 factors of reconciliation, which reflected what we learned from Elders, Survivors, and reconciliation leaders. A national sample of 592 Indigenous and 1,018 non-Indigenous participants completed the initial item pool. Exploratory factor analyses indicated that a 13-factor model had excellent fit, with only two factors needing minor conceptual modifications. We retained 64 internally consistent items representing 13 factors of reconciliation: Good Understanding of the Past and Present, Acknowledgment of Government Harm, Acknowledgment of Residential School Harm, Acknowledgment of Ongoing Harm, Engagement, Mutually Respectful Relationships, Nation-to-Nation Relationships, Personal Equality, Systemic Equality, Representation and Leadership, Indigenous Thriving, Respect for the Natural World, and Apologies. In Study 2, a national sample of 599 Indigenous and 1,016 non-Indigenous participants completed the retained items. The hypothesized factors had excellent fit, and the factor structure did not differ between Indigenous and non-Indigenous participants. We conclude by discussing contributions to social-psychological conceptualizations of reconciliation and how to use the Canadian Reconciliation Barometer to monitor social change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".