Bibliographic record
Abstract
This essay offers a set of strategies for utilizing the words of survivors and of witnesses to genocide in the classroom. Including the voices of survivors and victims in our classroom conversations about genocide, its impact, representation, and the possibilities for its prevention is crucial to an ethical and wholistic pedagogy of genocide. Discussion of these events in the classroom often finds us confronting questions from students about truth, historical accuracy, authenticity, and authority. Addressing such questions requires careful framing that takes into account student assumptions and cultural discourses about memory and witnessing, as we work with students to develop a shared vocabulary that accounts both for the individual survivor or witness, as well as often invisible issues in the study of testimony such as technical presentation, editing, and genre. This paper argues for the importance of working with students to develop a critical classroom vocabulary for analyzing both written and audio-visual testimonies in the classroom. Drawing on a number of conversations and using examples from assignments developed by the participants in the 2021 Silberman Seminar, this essay explores and reflects on several classroom exercises and activities for using survivor testimony in the classroom, and for navigating the multiple kinds of truth that are implicated in testimony. Acknowledging and analyzing the construction of these testimonies allows students not only a deeper understanding of the survivor and their experiences, but also great insight into how testimony, as a genre, as text and media, and as a discourse, shapes our encounters with survivors and their memories.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".