RECONCILING HISTORICAL INJUSTICES: EXPLORING THE DYNAMICS OF RECONCILIATION IN COLONIAL MEMORY POLITICS
Bibliographic record
Abstract
While existing scholarship has explored reconciliation efforts in post-conflict societies, gaps remain in understanding how countries reconcile historical injustices, especially those stemming from colonial legacies. This study draws from existing theoretical frameworks to uncover reconciliation processes by comparing countries with internal colonization (settler colonialism) and external colonization (imperialism). The research examines whether countries like Canada, France, Mexico, Spain, the United Kingdom, and the United States show variations in their reconciliation efforts. Using a qualitative design, this analysis evaluates four key political reconciliation processes: formal apologies, acknowledgments of historical injustice, engagement with truth and reconciliation commissions, and enactment of restorative justice policies. The findings reveal that settler colonial states have more robust and structured approaches to these processes than imperial states. This contributes to a deeper understanding of reconciliation and its implications for fostering peace and societal transformation after historical injustices, offering insights for future research into colonial reconciliation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".