Bibliographic record
Abstract
Kevin Kerr’s Unity (1918) profiles a small town in Saskatchewan, Canada, navigating the 1918 “Spanish flu” pandemic. The play has been produced over 100 times and received the 2002 Governor General’s Award for English-language drama. While there has been a new surge of interest in the play since the COVID-19 pandemic, the existing scholarship on Kerr’s text has focused overwhelmingly on its status as a war play rather than its epidemiological core subject matter, as the action takes place in the last days of World War I and its aftermath. This article places the play instead within the frames of influenza scholarship and the medical humanities in an attempt to recover and animate the spectres of the Spanish flu pandemic and give voice to the ignored victims of the largest mass death in history. By emphasizing the bodily aspects of influenza and its attendant cultural challenges and behaviours, Kerr’s play offers a poetics of porous exchange that defies traditional distinctions between culture and biology. It does this by foregrounding and exploring the unique attributes of the 1918 pandemic, by charting new ground in the representation of illness and erotic and sexual expression, and by offering a hybrid dramaturgical structure influenced by the interface between theatrical form and virological contagion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".