Bibliographic record
Abstract
In this thoughtful essay, its author Elizabeth McElroy explores Margaret Edson’s celebrated play “Wit” with a focus on biomedical ethics, namely, the attitudes toward the dying that the play’s hospital setting might encourage or, indeed, discourage. McElroy’s opening gambit is to set Edson’s play alongside Freud’s claims regarding the potential for theatre to expose its spectators to the experience of death. Pace Freud, we are all hardwired to avoid any sense of our own death for the deceptively simple reason that consciousness literally cannot grasp it; try as we might to imagine our own deaths, we are always there as spectators in that very act of imagining. Despite Freud’s own ambivalence to the powers of actual theatrical spectatorship—a spectatorship that both brings death closer and holds us at a safe distance from it— McElroy resolves to deepen and complicate Freud’s idea of spectatorship by exploring the various modes of looking that are played out in “Wit”: the observational power of the attending physician-researchers, for whom the protagonist Vivian Bearing is ‘research’; the compassionate regard of the duty nurse; and finally, the play’s breaking of the fourth wall, which allows for a more fundamental experience of looking at the dying, one that is rendered all the more complex because it is accompanied by the experience of being looked at by the dying. In a skillful treatment, McElroy ties the play’s different modes of looking to recent bioethical considerations of best practice in terms of patientphysician relationships. —Sarah Clift
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".