Representations of Home: A Study of Memory and Trauma
Bibliographic record
Abstract
This thesis analyzes three Canadian Sri Lankan writers’ representations of “Home” in Running in the Family by Michael Ondaatje, Funny Boy by Shyam Selvadurai, The Boat People by Sharon Bala and Anil’s Ghost also by Ondaatje. Most of the novels capture some of the “crucial junctures” in Sri Lankan history that intersect with political, ethnic, and national conflict; and how traversing these intersections causes trauma in the characters. Each writer in their text examines Sri Lankan history from a distance, while renegotiating their characters’ ties to their homeland. I examine existing theory by Susan Stanford Friedman and Vijay Agnew as they define what a home is, and look at displacement and belonging simultaneously to examine what they have to say about the home as a construct. In my thesis, I explore how each writer reinvigorates what “home” means to their characters, via the fictional representations of their emotional and expatriate longings, through memory, trauma and nostalgia. I particularly focus on these four texts by referring to Marianne Hirsch’s discussion of “postmemory” and Edward Mallot’s theory examining the role of witness writing in each text. Finally, I consider the role of the body in transferring memory and trauma, both in the representations of literal bodies of slain characters, but also through the recollected memory of forebears, and how those familial predecessors transfer history by creating witnesses to their memories and trauma.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".