Bibliographic record
Abstract
Truth Commissions have come to be regarded as a turning point for post-conflict and post-authoritarian states in transition. In this article, I argue that truth commission testimony, broadly defined to include artistic, cultural, and media productions, must be experienced as forms of affective materiality over discursive inscription. Using as an instrumental case study the Truth and Reconciliation Commission of Canada (2008–2015), I conceptualize testimony as a necessary re-fictionalization of the past, present, and future of a nation. The truth commission discourse, especially in Canada, works to protect the perpetrators by (1) disallowing their identities from entering into the public record, and (2) creating bystanders out of those perpetrators that allows for an innocent and ineffective witnessing. The push for forgiveness harnesses an imperative for truth commissions to idealize and idolize the emotional moment of testimony. It is imperative to resist the spectacle of confession and testimony. But the witness must not be discarded. The witness must be found in those cultural institutions beyond truth commission events to include the aesthetics of reconciliation.
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 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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.126 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".