Truth Commissions and Public Inquiries: Addressing Historical Injustices in Established Democracies
Bibliographic record
Abstract
In recent decades, the truth commission has become a mechanism used by states to address historical injustices. However, truth commissions are rarely used in established democracies, where the commission of inquiry model is favoured. I argue that established democracies may be more amenable to addressing historical injustices that continue to divide their populations if they see the truth commission mechanism not as a unique mechanism particular to the transitional justice setting, but as a specialized form of a familiar mechanism, the commission of inquiry. In this framework, truth commissions are distinguished from other commissions of inquiry by their symbolic acknowledgement of historical injustices, and their explicit “social function” to educate the public about those injustices in order to prevent their recurrence. Given that Canada has established a Truth and Reconciliation Commission (TRC) on the Indian Residential Schools legacy, I consider the TRC’s mandate, structure and ability to fulfill its social function, particularly the daunting challenge of engaging the non-indigenous public in its work. I also provide a legal history of a landmark Canadian public inquiry, the Mackenzie Valley Pipeline Inquiry, run by Tom Berger. As his Inquiry demonstrated, with visionary leadership and an effective process, a public inquiry can be a pedagogical tool that promotes social accountability for historical injustices. Conceiving of the truth commission as a form of public inquiry provides a way to consider the transitional justice literature on truth commissions internationally along with the experiences of domestic commissions of inquiry to assemble strategies that may assist the current TRC in its journey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".