Tracing the Lost Bodies: Testimony, Witnessing, and Trauma in Billydéki
Bibliographic record
Abstract
The last residential school in Canada, for Aboriginal students, was shut down in 1996; however, its percussions has affected not only the survivors but their next generations. By reading literary works, the audience can be informed of what happened in the past. The literary work merges narrativization and history to the extent that literature is involved in the action of creating a new form of narrative testimony to rethink historical incidents. History is transformed by bearing literary witness to residential schools in Canada. Sonia Perron's Billydéki (2019), not only bears literary witness to residential schools in Canada but also indicates the transformational relationship between narrative and history. In reading Billydéki, there is the possibility of finding out what residential schools have done to Aboriginal communities. Its language transmits eye witness's direct experience through the various characters at the moment of abuse and, therefore, historicize these incidents. The narrative does not tell, this is what happened, but it shows us through the eyes of various characters who were present at the moment of trauma; either being engaged in it or having onlooker stand. The aim of this paper is applying Judith Herman’s concept of trauma and Shoshana Felman and Dori Laub’s concept of testimony and witnessing to Sonia Perron's Billydéki, in order to shed light on the historical incidents that happened at residential schools and left unspoken for some time.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".