Bibliographic record
Abstract
Digital first-person testimonies have become increasingly more available and utilized to engage students. However, with its increase, digital first-person testimonies are facing significant ethical and pedagogical challenges, especially within the field of Holocaust Education which has historically relied on survivor testimony (Ballis, et. al., 2025; Marcus, et. al., 2021; McGregor, et. al., 2022; Tirosh & Mikel-Arieli, 2023; Traum, et. al., 2015; Walden, 2021). With the era of living survivors rapidly coming to an end, understanding the role of digital first-person testimonies within Holocaust Education is paramount. This article summarizes the work of a larger empirical study on the use of Virtual Interactive Holocaust Survivor Testimony (VIHST) in place of live Holocaust survivor testimony at the National Holocaust Centre and Museum (UK). The overview of the findings answers two research questions concerning the implementation of VIHST at the National Holocaust Centre and Museum (UK): 1) How do stakeholders perceive the value, utility, and challenges of learning from and with VIHST? 2) What are the interactional forces shaping pedagogical decisions around the use of VIHST in museums?
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.021 |
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".