Bibliographic record
Abstract
This paper considers the implications of teaching narratives about trauma in a university setting. As researchers begin to teach narratives which are part of what is beginning to be called “trauma studies,” it is necessary to think about how to teach narratives about atrocity. I argue that narratives like Elly Danica’s Don’t: A Woman’s Word do not portray trauma in a confessional mode, but seek to enact secondary trauma within readers or viewers as part of an individual or social transformation. This enacting produces a specific type of silence. In this silence, the readers or viewers are called into being as witnesses in very special ways. The pedagogical challenge which teachers of testimonial narratives of trauma face is twofold. First, there is the challenge of silence as a necessary part of the response to trauma, and second, there is the ethical challenge of bearing witness to trauma in a classroom situation. I consider work by Dori Laub on trauma witnessing and challenge assumptions by Shoshana Felman about teaching to a crisis in order to see how students can become witnesses.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".