Memory and postmemory in the writing of North American writers of Lithuanian descent
Bibliographic record
Abstract
This dissertation analyzes five memoirs written in English by North American writers of Lithuanian descent, published between 2001 and 2017 in the United States and Canada: Painted in Words—A Memoir (2001) by Samuel Bak, The Barefoot Bingo Caller (2017) by Antanas Sileika, White Field, Black Sheep: A Lithuanian-American Life (2010) by Daiva Markelis, A Guest At the Shooters’ Banquet (2015) by Rita Gabis, and Siberian Exile: Blood, War, and a Granddaughter’s Reckoning (2017) by Julija Šukys. In their memoirs, these writers reflect on and challenge aspects of Lithuanian historical trauma (the Holocaust) and Lithuanian cultural trauma (deportations to Siberia, World War II, the postwar experience, the refugee experience, immigration). They consider the impact of those historical events on their ancestors, themselves, and their descendants. All five embarked on postmemory rite of return journeys to Lithuania after Lituania’s independence from the Soviet Union, seeking answers about family narratives. These memoirs function both as memory and postmemory narratives. In each of the memoirs, the writers explore personal narratives as narratives of culture against the backdrop of collective memory and historical and cultural trauma. Superimposed onto their individual memory narratives are explorations of familial and affiliative postmemory that are geographically, historically, and culturally distant for these writers living on the North American continent, and yet vitally important to them. There is a belatedness to these memory narratives, which in part is caused by the divisions of the Iron Curtain and Cold War politics, and in part because of the time needed to process and heal from extreme trauma (the first generation) or come to terms with inherited postmemory traumas (the second and third generations). Writing in English about Lithuania, these writers function as cultural translators who translate their postmemory experience for an audience of North American readers. Their work is representative of a growing body of literary novels, memoirs, essays, plays, and poems that explore postmemory topics related to Lithuanian cultural and historical trauma and collective memory that are written in English and published in North America by both university presses and commercial publishers.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".