Bibliographic record
Abstract
How do writers portray the absence of one or more of our senses? In particular, how do they convey the absence of hearing? The question arises for me because I was born deaf. While I have the occasional complex or awkward experience because the world is designed by the hearing for the hearing, I have never experienced the sensation of my other-hearingness as a grief or a loss. Instead, I experience my deafness as another sensory perception: different from the hearing person, but a sense all the same. In this essay, I take up the challenge of examining the portrayal of deafness in contemporary fiction by comparing Vikram Seth’s novel, An Equal Music, in which the heroine, Julia, is a deaf concert pianist, with Frances Itani’s novel, Deafening, a fictionalized account of a Canadian deaf woman, Grania. I show how, in Seth’s novel, the reader witnesses the impact of hearing loss on Julia through the observations and experiences of Michael, her former lover and fellow musician. In Itani’s novel, I show how the reader is vicariously immersed in the experience of deafness through the cumulative impact of the omnipotent (and apparently hearing) narrator’s reports of the reactions of the other characters to Grania’s deafness, together with Grania’s interior monologue in which she reports her own observations of hearing people’s reactions to her deafness. Of course, while some of my observations inevitably draw on my particular insights as a deaf reader, this does not mean my observations are representative of all deaf people (just as one hearing reader is not representative of all hearing people).
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 | 0.002 | 0.008 |
| 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 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".