Mirroring Truths: How Liberal Democracies Are Challenging Their Foundational Narratives
Bibliographic record
Abstract
Long-established liberal democracies with histories of settler colonialism—from the United States and Canada to Australia and Scandinavia—are beginning to explore their histories of violence and dispossession. This, in many ways, is long overdue, but the desire to come to terms with past injustices should not obscure the challenges that still stand in the way of any reasonable effort to do so. We argue that transitional justice can be applied to colonial history in liberal democracies, but there are major conceptual and practical obstacles that need to be overcome if this is to happen in meaningful ways. We explore three of these obstacles here that are particularly significant: the doctrine of intertemporal law, the unequal power balance between the Global North and the Global South, and national identity. If these are to be overcome, it is important to tie historical to present injustices and to incorporate, beyond violations of physical rights, violations of economic and social rights that are particularly relevant for understanding continuities between past and ongoing violations. These rights are commonly neglected even by states that recognize a broad set of liberal rights and have the capacity to ensure that they are realized, and represent a promising avenue for pursuing a truly inclusive, equitable, and universal understanding of justice.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.098 |
| Scholarly communication | 0.033 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".