Post-Translation and Holocaust Memory in Social Media: The Case of Eva Stories
Bibliographic record
Abstract
This article aims to show that, in the digital age, traditional definitions of meaning, text, and translation are insufficient to reflect the virtual reality in which digital texts circulate. Virtual social networks are an ideal space for the (re)production of discourses and (re)presentation of identities and cultures. Just as the so-called shift from the monomodal to the multimodal represents a turning point in our way of perceiving meaning, text, and translation, the prevalent use of new media and screen-based communication requires new translation paradigms that reject the pre-established dichotomies of the discipline and attend to the multimodal, open, and fluid character of the texts that we receive, read, and share daily. With the proliferation of mediations and intermediaries between events and their narration, it is not unreasonable to question today how these events have been translated and how their discourses are post-translated within this digital space. In order to show how new texts are remediated and constitute rewritings and post-translations, this paper analyzes Eva Stories (Instagram, 2019), a mini-series launched on Instagram Stories, as a post-translation of Éva Heyman’s diary (Zsolt, 1948) remediated and retold through a different medium and in a completely different context. Éva was a 13-year-old victim of the Holocaust. This event, though repeatedly defined as ineffable, has been represented, rewritten, translated, and post-translated in countless ways, thereby giving rise to on-going ethical debates the article also seeks to address.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| 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".