The Trauma of “Fear-Induced Exodus:” The Case of Victor Magiar and the Italian Jews of Libya
Bibliographic record
Abstract
The Italian/Italophone Jewish community is amongst those that suffered from the Holocaust and other traumas. Drawing on the work of thinkers of trauma theory such as Dori Laub and Cathy Caruth, this paper aims to add to the current discourse on literary production by Italian/Italophone Jews by analyzing the trauma of the Italian Jewish community in postcolonial Libya, a topic often neglected by scholars. In 1967, the long-established Jewish community in Libya was forced to leave, abandoning all its property and economic funds. Victor Magiar, a Sephardic Jew born in Libya in 1957, was among those who — like all Jews who lived in Arabic lands — experienced trauma due to a myriad of factors, such as pogroms and the fact that he had no passport and true nationality. Through Magiar’s novel E venne la notte: Ebrei in un paese arabo (2003), this paper examines the trauma of the “fear-induced exodus” to Italy on the writer and his community. Moreover, a continuous dialogue with the author informs the analysis of the trauma involved in his story and the Sephardi community history, which also includes the elucidation of Jewish identity in postcolonial Libya. This paper highlights the details of history and stories that go beyond the novel itself, illuminating a nearly unknown facet of Italian history and of the country’s current multilingual and multicultural society.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.028 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".