Bibliographic record
Abstract
This chapter details the life story and (post)memories of the first Israeli participant, Ran Vered, who is the study’s oldest interviewee and the only one who does not share photographs alongside his storytelling. As a member of the immediate Holocaust postmemory generation, Ran grew up in the early years of the new Israeli state surrounded by survivors who rarely shared stories of the horrors they endured. Yet, as Ran explains, the Holocaust was embedded into the everyday fabric of his childhood and ultimately led him to serve in the Israeli Defense Forces (IDF) for approximately eight years, more than twice as long as his fellow Israeli participants. Like most Israelis of his generation, Ran grew up feeling that Israel was an underdog and that he had no choice but to protect his country from both another holocaust and threatening Arab neighbors. While his (post)memories often slip in and out of problematic master narratives and collective memories of the Holocaust, Ran makes clear connections between his military service and the Holocaust, critiques the Israeli education system while recounting his efforts to re-educate himself about the events of 1948 and the Nakba, and expresses his willingness to try a two-state solution.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.129 | 0.038 |
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".