CONSOLIDATED DEMOCRACIES AND THE PAST: TRANSITIONAL JUSTICE IN SPAIN AND CANADA
Bibliographic record
Abstract
The majority of scholarly consideration on the principles of transitional justice has focused upon how emerging democracies should deal with former regimes immediately following violent conflict. However, consolidated democracies have also begun to turn to transitional justice mechanisms in order to address historical legacies of violence and repression. This article examines Spain and Canada, two countries dealing with seemingly disparate issues: the legacy of the Civil War and Franco’s repressive regime, and the abuses of the Indian Residential Schools system, respectively. However, both nations have been forced to respond to similar questions regarding the merits of revisiting a painful past well after democratic consolidation. The article first discusses the proliferation of transitional justice principles into consolidated democracies, and considers the argument that such processes may destabilize and divide society, particularly by exacerbating federalist divisions. It concludes that despite the unique challenges of employing transitional justice so long after a transition, the Spanish and Canadian cases reveal the inevitability of confronting the past in response to charges of hypocrisy and illegitimacy. Consolidated democracies, embedded with principles of public contention and debate, are well-suited to respond to these challenges while maintaining political and societal cohesion.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".