Truth-Telling by Wrong-Doers? The Construction of Avowal in Canada’s Truth and Reconciliation Commission
Bibliographic record
Abstract
The truth commission has emerged in the last thirty years as a distinct juridical form that views the production of truth as necessary, and in some cases sufficient, for achieving justice. In his history of truth-telling in juridical forms, Michel Foucault conducts a genealogy of avowal (or confession) in western judicial practice; critical to his definition of avowal is that the truth-teller and wrong-doer must be the same subject. In my analysis, I consider avowal in light of a relatively recent judicial innovation: the truth commission, with Canada’s Indian Residential Schools Truth and Reconciliation Commission (TRC) as a particular case. The TRC’s emphasis on the testimony of victims rather than perpetrators means that truth-telling and wrong-doing are decoupled in this juridical form, suggesting that avowal is not a function of truth commissions according to Foucault’s criteria. Does this mean that truth commissions are not involved in truth production, or perhaps that they are not a juridical form in the lineage of those examined by Foucault? The truth commission is a juridical form that Foucault was unable to address because it developed only after his death, and it is possible that it challenges his core understanding of avowal; however, the truth commission also appears to be consistent with trends that he predicted about the role of truth-telling in the modern judicial system.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.048 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".