<i>Exercises in Loss</i> by Agata Tuszyńska as a Poetic Rendition of the Untellable Experience of Co-Dying
Bibliographic record
Abstract
‘We died on the 16th of September 2006 at 4.32 pm’ writes Agata Tuszyńska in her memoir Exercises in Loss (2007) which narrates the dying of her husband, a writer and Polish émigré living in Canada. Her narrative offers a poetic rendition of his dying of terminal illness, during which time she became not only a caring companion but also a co-dying partner. This article examines the ways in which Exercises in Loss provides a novel perception of narrating loss, not only in the genre of bereavement memoir but also through a conscious stretching of the prose narrative towards poetic diction, simultaneously blurring the edges between life and death and between the living and the dying/dead. Her text is therefore an epitaph to love and an elegy to her loved and loving husband, but also an innovative way of writing about how ‘our life stories are not merely about us but in an inescapable and profound way are us’ (Eakin [2008]. Living Autobiographically: How We Create Identity in Narrative. Ithaca, NY: Cornell University Press, x, emphasis original ). The article delineates the devices Tuszyńska uses to prove that her memoir is not a mere interpretation of her partner’s suffering and departure but also a self-reflective narrative of her own (partial, complete, embodied, spiritual) dying.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".