Bibliographic record
Abstract
Why are so many contemporary poets writing elegies? Given a century shaped by two world wars, vast population displacements, and shifting attitudes towards aging and death, is the elegy form adaptable to the changing needs of writers and audiences? In a sceptical age, where can consolation be found? In We Are What We Mourn Priscila Uppal examines why and how the work of mourning has drastically changed in the latter half of the twentieth century, focusing on the strong pattern in contemporary English-Canadian elegy that emphasizes connection rather than separation between the living and the dead. Uppal offers a penetrating reading of Canadian elegies that radically challenges English and American elegy traditions as well as long-standing psychological models for successful mourning. She sets up useful categories for elegy study - parental elegies, elegies for places, and elegies for cultural losses and displacements - and suggests where elegy and mourning studies might be headed post 9/11. The first book on the Canadian poetic elegy, We Are What We Mourn challenges all previous ideas about the purpose of mourning and will intrigue anyone interested in how mourning shapes cultural identity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".