<i>In the Maelstrom of History: A Conversation with Miriam</i> Rosanna Turcinovich Giuricin
Bibliographic record
Abstract
In the Maelstrom of History: A Conversation with Miriam by Rosanna Turcinovich Giuricin records the captivating story of Miriam Grünglas, a Holocaust survivor born in Tyachiv, Slovakia, who grew up in Trieste, Italy. With the promulgation of the racial laws in Italy in 1938, she and her family were forced to leave Trieste. They headed back to Tyachiv, where quite soon they ended up deported to Auschwitz. Miriam is the only survivor of her family. At the end of the war, she went to Prague, where she stayed for three years. In 1948, she emigrated to Canada and started a new life. In the Maelstrom of History provides English readers with an elegant translation of Maddalena ha gli occhi viola, the original Italian publication of Miriam’s story. It was published on Holocaust Remembrance Day, January 27, 2016. Turcinovich Giuricin narrates Miriam’s story by rewriting a series of interviews she carried out with her, intertwining her own perspective with Miriam’s—“Bullying is a dangerous seed, especially if it has racial overtone,” Turcinovich Giuricin offers, with Miriam responding that it is “to be fought against with all our strength” (p. 17). The author engagingly records their conversations, plainly adds indirect quotations and reflections related to the literature devoted to Holocaust Studies, and creatively re-elaborates direct dialogues by rewriting Miriam’s words. She often interrupts the reconstruction of the events and intervenes in the text when she poses questions—“Wait, Miriam, wait!…. But why do you speak of hospitals?” (p. 18)—thereby enhancing a feeling of kinship with the reader. The narrative seems to be built on different continuous streams of consciousness, resulting in a distinctive piece of mixed genre writing, intertwining patterns typical of interviews with elements traceable to testimonial literature, such as a memoir.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".