Beyond the "talking cure": narrative alternatives for telling trauma in Canadian fiction
Bibliographic record
Abstract
This study explores narrative analeptics for trauma in contemporary English-Canadian fiction by Michael Ondaatje, Eden Robinson, Elyse Friedman, and Joy Kogawa. The introduction situates fictional works by these authors between two parallel yet contradictory narratives regarding the efficacy of the "talking cure": in current discourses of trauma, this speech act is designated as both a superior curative agent and an unsatisfactory remedy that is incapable of representing the traumatic event. My thesis primarily uses Canadian fiction to nuance assumptions inherent in both poles of these arguments, ultimately exploring the conceptual space between them. Chapter three investigates the ways in which the practical joke is adopted in lieu of the "talking cure" in both Elyse Friedman's Then Again and Eden Robinson's "Queen of the North," the closing story from her collection Traplines. This chapter primarily focuses on the status of the practical joke as a narrative paradigm and cultural artifact. In chapter four, my analysis of Joy Kogawa's Obasan considers poetry as an alternative to talk therapy. It discusses the ways Kogawa's poetic novel is a testimony that deviates from established aesthetics of the "talking cure." My conclusion suggests that we begin to approach the subject of trauma and cure with a different set of hermeneutic practices, so that rather than focusing on the intellectual complexities and aporias that surround psychopathology, we begin to consider narrative possibilities for telling trauma. The first chapter argues that in Trauma and Recovery, Judith Herman's efforts to centralize the "talking cure" or "abreaction" as a panacea for trauma result in a delimited reading of the critics whom she cites to support her summations, omitting mention of their ambivalence regarding the efficacy of integrative abreaction. This elision encourages a reconsideration of the seemingly harmonious relationship presented in her work between the ethics of representation and imperative for effective personal treatment. Chapter two continues to question the efficacy of the "talking cure" through an analysis of Michael Ondaatje's novels In the Skin of a Lion and The English Patient. It explores Ondaatje's deviation from the therapeutic priorities of current discourses of trauma by tracing his developing disillusionment with story telling as a curative agency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".