Dialogic Interactions: Traumatic Narratives of Forced Removal Inscribed in Archives and Memoirs
Bibliographic record
Abstract
Dialogic Interactions: Traumatic Narratives of Forced Removal Inscribed in Archives and Memoirs explores the dialogic interaction that takes place between memoirs and archives during three distinct moments in Canadian history: Indian Residential Schools, Japanese Canadian internment and Jewish Canadian internment. This project pairs Edmund Metatawabin’s Up Ghost River: A Chief’s Journey Through the Turbulent Waters of Native History with the 1999 court transcript of Cree nun, Anna Wesley, Tom Sando’s Wild Daisies in the Sand with his Japanese diaries (which I commissioned to have translated into English) and Eric Koch’s Otto & Daria: A Wartime Journey Through No Man’s Land with letters from family and friends. Using Mikhail Bakhtin’s theory of the dialogic and heteroglossia as a foundation, this dissertation proposes a new theoretical framework for reading between memoirs and archives. This framework consists of dialogic citizenship, counternarratives, code switching and/or composition. While the chapters on Metatawabin and Sando engage with dialogic citizenship, counternarratives and code switching, the chapter on Koch introduces dialogic composition. This dissertation also engages with thinkers on national narratives such as Benedict Anderson, James Wertsch and Berber Bevernage. I argue that reading the memoirs and archives in tandem helps readers to challenge engrained national narratives, and also shows ideological shifts that would not be evident simply by engaging with one form. \n\nThese close, historically and politically informed readings of the memoirs and the archives reveal the power of rejoinder and response. As this dissertation shows, response does not need to take place between two people, but can take place with one person (at different moments in one’s life). Furthermore, the difference in forms (court transcript, diaries, and letters) present vital discussions of memory, time, language, accessibility, citizenship and belonging in drastically different settings. By engaging with some of the dialogic threads that exist between memoirs and archives, I argue that a generative space exists between them for readers. This critically challenging space not only forces readers to look inward at preconceived biases but also to engage with material that they might be culturally outside.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.048 | 0.055 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".