Looking Forward, Looking Back: The Canadian Truth and Reconciliation Commission and the Mackenzie Valley Pipeline Inquiry
Bibliographic record
Abstract
Abstract When we talk about truth and reconciliation commissions, we are accustomed to speaking of “transitional justice” mechanisms used in emerging democracies addressing histories of grave injustices. Public inquiries are usually the state response to past injustice in the Canadian context. The Canadian Truth and Reconciliation Commission (TRC) is the result of a legal settlement agreement involving the government, representatives of indigenous peoples who attended residential schools for a period lasting more than a century, and the churches that operated those schools. Residential schools have been addressed in a series of public inquiries in Canada, culminating in the TRC. I argue that some of Canada's previous public inquiries, particularly with respect to indigenous issues, have strongly resembled truth commissions, yet this is the first time that an established democracy has called a body investigating past human-rights violations a “truth commission.” This article considers some of the reasons for seeking a truth commission in an established democracy and looks to a previous public inquiry led by Thomas Berger, the Mackenzie Valley Pipeline Inquiry, for some useful strategies for the TRC as it pursues its mandate. In particular, I suggest that a commission can perform a social function by using its process to educate the broader public about the issue before it.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.068 | 0.056 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".